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The aims of this study were to explore the significant chemical changes in functional components induced by the traditional processing method and evaluate whether this method based on nine cycles of steaming and drying can effectively enhance the medicinal effects of Polygonatum cyrtonema rhizome. A global analysis on dynamic changes in secondary metabolites during nine processing cycles was performed, and the significantly differentially accumulated secondary metabolites were initially identified based on the secondary metabolome. Unsupervised principal component analysis (PCA), hierarchical clustering analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLA-DA) on secondary metabolites clearly showed that processing significantly increased the global accumulation of secondary metabolites in processed P. cyrtonema rhizomes compared to unprocessed crude rhizomes. The first six processing cycles induced drastic changes in the accumulation of functional components, while the last three did not induce further changes. The accumulations of most functional components were significantly enhanced after the first three cycles and stabilized after six cycles; meanwhile, the first three cycles also led to numerous new components. However, the enhancing effects were unavoidably reversed or weakened under continued processing lasting 6-9 cycles. Furthermore, continued processing also reduced the contents of a small number of original components to undetectable levels. Processing induced some significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, among which the first three processing cycles enhanced the synthesis of various secondary metabolites and significantly affected the metabolisms of amino acids. In conclusion, this study not only reveals that processing can effectively enhance the medicinal effects, by two main mechanisms including enhancing chemical synthesis and inducing structural transformation of functional components, but also provides theoretical guidance for the optimization of the traditional processing method based on nine cycles of steaming and drying for achieving optimal effects on enhancing the medicinal effects of P. cyrtonema rhizome.

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The success of obtaining solid dispersions for solubility improvement invariably depends on the miscibility of the drug and polymeric carriers. This study aimed to categorize and select polymeric carriers via the classical group contribution method using the multivariate analysis of the calculated solubility parameter of RX-HCl. The total, partial, and derivate parameters for RX-HCl were calculated. The data were compared with the results of excipients (N = 36), and a hierarchical clustering analysis was further performed. Solid dispersions of selected polymers in different drug loads were produced using solvent casting and characterized via X-ray diffraction, infrared spectroscopy and scanning electron microscopy. RX-HCl presented a Hansen solubility parameter (HSP) of 23.52 MPa1/2. The exploratory analysis of HSP and relative energy difference (RED) elicited a classification for miscible (n = 11), partially miscible (n = 15), and immiscible (n = 10) combinations. The experimental validation followed by a principal component regression exhibited a significant correlation between the crystallinity reduction and calculated parameters, whereas the spectroscopic evaluation highlighted the hydrogen-bonding contribution towards amorphization. The systematic approach presented a high discrimination ability, contributing to optimal excipient selection for the obtention of solid solutions of RX-HCl.

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Evidence for the tumour-supporting capacities of the tumour stroma has accumulated rapidly in colorectal cancer (CRC). Tumour stroma is composed of heterogeneous cells and components including cancer-associated fibroblasts (CAFs), small vessels, immune cells, and extracellular matrix proteins. The present study examined the characteristics of CAFs and collagen, major components of cancer stroma, by immunohistochemistry and Sirius red staining. The expression status of five independent CAF-related or stromal markers, decorin (DCN), fibroblast activation protein (FAP), podoplanin (PDPN), alpha-smooth muscle actin (ACTA2), and collagen, and their association with clinicopathological features and clinical outcomes were analysed. Patients with DCN-high tumours had a significantly worse 5-year survival rate (57.3% versus 79.0%; p = 0.044). Furthermore, hierarchical clustering analyses for these five markers identified three groups that showed specific characteristics: a solid group (cancer cell-rich, DCNLowPDPNLow); a PDPN-dominant group (DCNMidPDPNHigh); and a DCN-dominant group (DCNHighPDPNLow), with a significant association with patient survival (p = 0.0085). Cox proportional hazards model identified the PDPN-dominant group (hazard ratio = 0.50, 95% CI = 0.26-0.96, p = 0.037) as a potential favourable factor compared with the DCN-dominant group. Of note, DCN-dominant tumours showed the most advanced pT stage and contained the lowest number of CD8+ and FOXP3+ immune cells. This study has revealed that immunohistochemistry and special staining of five stromal factors with hierarchical clustering analyses could be used for the prognostication of patients with CRC. Cancer stroma-targeting therapies may be candidate treatments for patients with CRC.

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INTRODUCTION: Over the past decade, classifications using immune cell infiltration have been applied to many types of tumors; however, mesotheliomas have been less frequently evaluated. METHODS: In this study, 60 well-characterized pleural mesotheliomas (PMs) were evaluated immunohistochemically for the characteristics of immune cells within tumor microenvironment (TME) using 10 immunohistochemical markers: CD3, CD4, CD8, CD56, CD68, CD163, FOXP3, CD27, PD-1, and TIM-3. For further characterization of PMs, hierarchical clustering analyses using these 10 markers were performed. RESULTS: Among the immune cell markers, CD3 (p < 0.0001), CD4 (p = 0.0016), CD8 (p = 0.00094), CD163+ (p = 0.042), and FOXP3+ (p = 0.025) were significantly associated with an unfavorable clinical outcome. Immune checkpoint receptor expressions on tumor-infiltrating lymphocytes such as PD-1 (p = 0.050), CD27 (p = 0.014), and TIM-3 (p = 0.0098) were also associated with unfavorable survival. Hierarchical clustering analyses identified three groups showing specific characteristics and significant associations with patient survival (p = 0.016): the highest number of immune cells (ICHigh); the lowest number of immune cells, especially CD8+ and CD163+ cells (ICLow); and intermediate number of immune cells (ICInt). ICHigh tumors showed significantly higher expression of PD-L1 (p = 0.00038). Cox proportional hazard model identified ICHigh [hazard ratio (HR) = 2.90] and ICInt (HR = 2.97) as potential risk factors compared with ICLow. Tumor CD47 (HR = 2.36), tumor CD70 (HR = 3.04), and tumor PD-L1 (HR = 3.21) expressions were also identified as potential risk factors for PM patients. CONCLUSION: Our findings indicate immune checkpoint and/or immune cell-targeting therapies against CD70-CD27 and/or CD47-SIRPA axes may be applied for PM patients in combination with PD-L1-PD-1 targeting therapies in accordance with their tumor immune microenvironment characteristics.

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(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

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OBJECTIVES: Modular architecture of traits in complex organisms can be important for morphological evolution at micro- and sometimes macroevolutionary scales as it may influence the tempo and direction of changes to groups of traits that are essential for particular functions, including food acquisition and processing. We tested several distinct hypotheses about craniofacial modularity in the hominine skull in relation to feeding biomechanics. MATERIALS AND METHODS: First, we formulated hypothesized functional modules for craniofacial traits reflecting specific demands of feeding biomechanics (e.g., masseter leverage/gape or tooth crown mechanics) in Homo sapiens, Pan troglodytes, and Gorilla gorilla. Then, the pattern and strength of modular signal was quantified by the covariance ratio coefficient and compared across groups using covariance ratio effect size. Hierarchical clustering analysis was then conducted to examine whether a priori-defined functional modules correspond to empirically recovered clusters. RESULTS: There was statistical support for most a priori-defined functional modules in the cranium and half of the functional modules in the mandible. Modularity signal was similar in the cranium and mandible, and across the three taxa. Despite a similar strength of modularity, the empirically recovered clusters do not map perfectly onto our priori functional modules, indicating that further work is needed to refine our hypothesized functional modules. CONCLUSION: The results suggest that modular structure of traits in association with feeding biomechanics were mostly shared with humans and the two African apes. Thus, conserved patterns of functional modularity may have facilitated evolutionary changes to the skull during human evolution.

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The present study analyzed the expression of five independent immunohistochemical markers, CD4, CD8, CD66b, CD68, and CD163, on immune cells within the colorectal cancer (CRC) tumor microenvironment (TME). Using hierarchical clustering, patients were successfully classified according to significant associations with clinicopathological features and/or survival. Patients with mismatch repair-proficient (pMMR) CRC were categorized into four groups with survival differences (p = 0.0084): CD4Low , CD4High , MΦHigh , and CD8Low . MΦHigh tumors showed significantly higher expression of CD47 (p < 0.0001), a phagocytosis checkpoint molecule. These tumors contained significantly greater numbers of PD-1+ (p < 0.0001), TIM-3+ (p < 0.0001), and SIRPA+ (p < 0.0001) immune cells. Notably, 10% of the patients with pMMR CRC expressed PD-L1 (CD274) on tumor cells with significantly worse survival (p = 0.00064). The Cox proportional hazards model identified MΦ High (hazard ratio [HR] = 2.02, 95%, p = 0.032), CD8Low (HR = 2.45, p = 0.011), and tumor PD-L1 expression (HR = 2.74, p = 0.0061) as potential risk factors. PD-L1-PD-1 and/or CD47-SIRPA axes targeting immune checkpoint therapies might be considered for patients with pMMR CRC according to their tumor cells and tumor immune microenvironment characteristics.

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Fourier transform infrared spectroscopy (FTIR) is an alternative microbial identification technique due to its faster analysis times and lower cost compared to molecular methods. In this study, forty-three fungal strains isolated from different Turkish traditional mold-ripened cheeses representing nine different Penicillium species (P. roqueforti, P. corylophilum, P. before, P. crustosum, P. spinulosum, P. rubens, P. brevicompactum, P. paneum, and P. solitum) were analyzed by using FTIR HTS-XT (High Throughput Screening Extension) method in the 4000-400 cm-1 wavenumber range. The spectra of the isolates were evaluated, and the chemical structures corresponding to the fungus-specific spectral regions were determined as fatty acids (3600-2800 cm-1), amide I and amide II of proteins and peptides (1740-1500 cm-1), polysaccharides (1200-900 cm-1) and carbohydrates (900-600 cm-1). The isolates were grouped according to the hierarchical clustering analysis (HCA) by applying chemometrics combined with FTIR spectroscopy. Results showed that FTIR spectroscopy has a high capability for rapid determination of cheese fungi based on their FTIR spectra.

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High-yield potential perennial crops, such as Miscanthus spp. and Arundo donax are amongst the most promising sources of sustainable biomass for bioproducts and bioenergy. Although several studies assessed the agronomic performance of these species on diverse marginal lands, research to date on drought and zinc (Zn) resistance is scarce. Thus, the objective of this study was to investigate the drought and Zn stress tolerance of seven novel Miscanthus hybrids and seven Arundo clones originating from different parts of Italy. We subjected both species to severe drought (less than 30%), and Zn stress (400 mg/kg-1 of ZnSO4) separately, after one month of growth. All plants were harvested after 28 days of stress, and the relative drought and Zn stress tolerance were determined by using a set of morpho-physio-biochemical and biomass attributes in relation to stress tolerance indices (STI). Principal component analysis (PCA), hierarchical clustering analysis (HCA) and stress tolerance indices (STI) were performed for each morpho-physio-biochemical and biomass parameters and showed significant relative differences among the seven genotypes of both crops. Heatmaps of these indices showed how the different genotypes clustered into four groups. Considering PCA ranking value, Miscanthus hybrid GRC10 (8.11) and Arundo clone PC1 (11.34) had the highest-ranking value under both stresses indicating these hybrids and clones are the most tolerant to drought and Zn stress. In contrast, hybrid GRC3 (-3.33 lowest ranking value) and clone CT2 (-5.84) were found to be the most sensitive to both drought and Zn stress.

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Major aryl hydrocarbon receptor (AhR) agonists were identified in extracts of blubber, liver, and muscle from six long-beaked common dolphins (Delphinus capensis) and one fin whale (Balaenoptera physalus) collected from Korean coastal waters using effect-directed analysis. Results of the H4IIE-luc bioassay indicated that the polar fractions of blubber and liver extracts from the fin whale exhibited relatively high AhR-mediated potencies. Based on full-scan screening with high-resolution mass spectrometry, 37 AhR agonist candidates, spanning four use categories: pharmaceuticals, pesticides, cosmetics, and natural products, were selected. Among these, five polar AhR agonists were newly identified through toxicological confirmation. Concentrations of polar AhR agonists in cetaceans were tissue-specific, with extracts of blubber and liver containing greater concentrations than muscle extracts. Polar AhR agonists with great log KOA values (>5) were found to biomagnify in the marine food chain potentially. Polar AhR agonists contributed 8.9% of the observed AhR-mediated potencies in blubber and 49% in liver. Rutaecarpine and alantolactone contributed significantly to the total AhR-mediated potencies of blubber, whereas hydrocortisone was a major AhR contributor in the liver of the fin whale. This study is the first to identify the tissue-specific accumulation of polar AhR agonists in blubber and liver extracts of cetaceans.

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QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.

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Xiaozhong Zhitong tincture (XZZTT), a prominent Traditional Chinese Medicine (TCM) formulation comprising 21 intricate herbal components, poses a challenge in terms of quality control due to its complex composition and the interplay of diverse chemical constituents. To address this issue, a comprehensive assessment strategy was devised by integrating chromatographic and electrochemical techniques to construct a multidimensional fingerprint for XZZTT samples. This study encompassed the evaluation of 42 XZZTT samples through a systematic quantitative fingerprinting method (SQFM), while also quantifying the concentrations of four specific compounds-Geniposide, Palmatine hydrochloride, Paeonol, and Chlorogenic acid. The experimental approach encompassed the establishment of fingerprints using High-Performance Liquid Chromatography (HPLC), Gas Chromatography (GC), and GC-HPLC tandem fingerprints methods. Furthermore, electrochemical fingerprints (ECFP) were established using the B-Z oscillation system, and eight characteristic parameters in the oscillation system were recorded and compared among samples. Hierarchical Clustering Analysis (HCA) was subsequently employed to classify the distinct fingerprints and compare outcomes from one-dimensional spectroscopy, GC-HPLC tandem chromatography, and the fusion fingerprints. Finally, Grey Relation Analysis (GRA) was harnessed to unravel the relationship between ECFP outcomes and peak areas in fusion fingerprints, facilitating predictions regarding the substances' reducing potency. In conclusion, the rational combination of multidimensional fingerprinting and multidimensional analysis provides a reliable and comprehensive method for the evaluation of XZZTT and its related products.

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In macromolecular structure determination using X-ray diffraction from multiple crystals, the presence of different structures (structural polymorphs) necessitates the classification of the diffraction data for appropriate structural analysis. Hierarchical clustering analysis (HCA) is a promising technique that has so far been used to extract isomorphous data, mainly for single-structure determination. Although in principle the use of HCA can be extended to detect polymorphs, the absence of a reference to define the threshold used to group the isomorphous data sets (the `isomorphic threshold') poses a challenge. Here, unit-cell-based and intensity-based HCAs have been applied to data sets for apo trypsin and inhibitor-bound trypsin that were mixed post data acquisition to investigate the efficacy of HCA in classifying polymorphous data sets. Single-step intensity-based HCA successfully classified polymorphs with a certain `isomorphic threshold'. In data sets for several samples containing an unknown degree of structural heterogeneity, polymorphs could be identified by intensity-based HCA using the suggested `isomorphic threshold'. Polymorphs were also detected in single crystals using data collected using the continuous helical scheme. These findings are expected to facilitate the determination of multiple structural snapshots by exploiting automated data collection and analysis.

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Background: Acute rejection persists as a frequent complication after kidney transplantation. Defining an at-risk immune profile would allow better preventive approaches. Methods: We performed unsupervised hierarchical clustering analysis on pre-transplant immunological phenotype in 1113 renal transplant recipients from the ORLY-EST cohort. Results: We identified three immune profiles correlated with clinical phenotypes. A memory immune cluster was defined by memory CD4+T cell expansion and decreased naïve CD4+T cell. An activated immune cluster was characterized by an increase in CD8+T cells and a decreased CD4/CD8 ratio. A naïve immune cluster was mainly defined by increased naïve CD4+T cells. Patients from the memory immune profile tend to be older and to have diabetes whereas those from the activated immune profile were younger and more likely to have pre-transplant exposure to CMV. Patients from the activated immune profile were more prone to experience acute rejection than those from other clusters [(HR=1.69, 95%IC[1.05-2.70], p=0.030) and (HR=1.85; 95%IC[1.16-3.00], p=0.011). In the activated immune profile, those without previous exposure to CMV (24%) were at very high risk of acute rejection (27 vs 16%, HR=1.85; 95%IC[1.04-3.33], p=0.039). Conclusion: Immune profile determination based on principal component analysis defines clinically different sub-groups and discriminate a population at high-risk of acute rejection.

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A gas chromatography-triple quadrupole mass spectrometry(GC-MS) method was established for the simultaneous determination of eleven volatile components in Cinnamomi Oleum and the chemical pattern recognition was utilized to evaluate the quality of essential oil obtained from Cinnamomi Fructus medicinal materials in various habitats. The Cinnamomi Fructus medicinal materials were treated by water distillation, analyzed using GC-MS, and detected by selective ion monitoring(SIM), and the internal standards were used for quantification. The content results of Cinnamomi Oleum from various batches were analyzed by hierarchical clustering analysis(HCA), principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) for the statistic analysis. Eleven components showed good linear relationships within their respective concentration ranges(R~2>0.999 7), with average recoveries of 92.41%-102.1% and RSD of 1.2%-3.2%(n=6). The samples were classified into three categories by HCA and PCA, and 2-nonanone was screened as a marker of variability between batches in combination with OPLS-DA. This method is specific, sensitive, simple, and accurate, and the screened components can be utilized as a basis for the quality control of Cinnamomi Oleum.

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Urban rainfall events can lead to the runoff of pollutants, including industrial, pesticide, and pharmaceutical chemicals. Transporting micropollutants (MPs) into water systems can harm both human health and aquatic species. Therefore, it is necessary to investigate the dynamics of MPs during rainfall events. However, few studies have examined MPs during rainfall events due to the high analytical expenses and extensive spatiotemporal variability. Few studies have investigated the occurrence patterns of MPs and factors that influence their transport, such as rainfall duration, antecedent dry periods, and variations in streamflow. Moreover, while there have been many analyses of nutrients, suspended solids, and heavy metals during the first flush effect (FFE), studies on the transport of MPs during FFE are insufficient. This study aimed to identify the dynamics of MPs and FFE in an urban catchment, using high-resolution monitoring and machine learning methods. Hierarchical clustering analysis and partial least squares regression (PLSR) were implemented to estimate the similarity between each MP and identify the factors influencing their transport during rainfall events. Eleven dominant MPs comprised 75% of the total MP concentration and had a 100% detection frequency. During rainfall events, pesticides and pharmaceutical MPs showed a higher FFE than industrial MPs. Moreover, the initial 30% of the runoff volume contained 78.0% of pesticide and 50.1% of pharmaceutical substances for events W1 (July 5 to July 6, 2021) and W6 (August 31 to September 1, 2021), respectively. The PLSR model suggested that stormflow (m3/s) and the duration of antecedent dry hours (h) significantly influenced MP dynamics, yielding the variable importance on projection scores greater than 1.0. Hence, our findings indicate that MPs in urban waters should be managed by considering FFE.

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The water source of the water diversion project from the Hanjiang River to the Weihe River is one of the most important drinking water sources in China. Its water quality is related to the water safety of the long-distance water diversion system from the Hanjiang to Weihe Rivers. In order to explore the spatiotemporal change trend of the water environment characteristics of the water source area and analyze the key factors that have a greater impact on it, this study collected 9 types of water environment physical and chemical parameters from 10 water quality monitoring sections from 2017 to 2019; the water environment characteristics of the water source area of the water diversion system from the Hanjiang River to the Weihe River were analyzed and evaluated by using the variance analysis method, the hierarchical cluster analysis method and the water quality identification index evaluation method. The results were as follows. (1) There was spatiotemporal heterogeneity in a number of physical and chemical parameters in the water body of the water source. In terms of time, the concentrations of CODMn, COD, BOD5 and F- were higher in the flood season (July-October) than in the non-flood season (November-June). The concentrations of DO, TP and TN in the non-flood season were higher than those in the flood season. Spatially, the concentration of physical and chemical parameters of the water body in the Huangjinxia Reservoir area was higher than that in the Sanhekou Reservoir area. (2) The water quality of the water source area was good. The comprehensive water quality reached the Class II water quality standard of surface water environmental quality. Time showed that the comprehensive water quality in the non-flood season was better than that in the flood season. Spatially, the overall water quality of the tributaries was better than that of the mainstream. TN is a key indicator that affects water quality. (3) The spatial and temporal differences in water quality in water source areas are mainly affected by factors such as rainfall, temperature and human activities. This study can provide a scientific and data basis for related research on maintaining and improving the quality of the ecological environment of the water source areas of the Hanjiang to Weihe River Water Diversion System.

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Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

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Richness in nutrients with an ample of the myco-bioactive molecules makes Pleurotus osteratus preferential mushroom. In this paper, we conducted a preliminary study on bio-assay-guided fractionation of dichloromethane:ethanol crude extract (1:1, v/v) of P. osteratus (CD) against human breast cancer cell line (MDA-MB-231). Later, CD and its potent hexane (H) and ethyl acetate (EA) fraction were screened against a panel of a human cancer cell lines. H fraction possesses higher cytotoxicity followed by EA and CD. Literature review revealed that polyphenol and ergosterol are the biomarkers found in P. osteratus and could responsible for its cytotoxic potential. Accordingly, hyphenated liquid chromatography with tandem mass spectrometry (LC-MS/MS)-based polyphenol and ergosterol-targeted myco-metabolite profiling of CD, H, and EA fractions were carried out. Despite being significantly rich in polyphenol and ergosterol content, EA fraction showed moderate cytotoxicity. Considering this, liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-QTOF/MS)-based untargeted myco-metabolite profiling of CD, H and EA fractions was further conducted to identify a new biomarker. Tentatively, 20 myco-metabolites were identified, belonging to the class of steroids, alkaloid, terpenoid, fatty alcohol, and polyketide. The myco-metabolite variabilities among potent samples in correlation to their in vitro anti-cancer activity was explored using the different chemometric tools: principal component analysis (PCA), hierarchical clustering analysis (HCA), and partial least square (PLS). A probable synergistic action among identified myco-metabolites (betulin, solanocapsine, ophiobolin F, linoleoyl ethanolamide, (13R,14R)-7-labdene-13,14,15-triol, asterosterol, cholest-5-ene, (3b,6b,8a,12a)-8,12-epoxy-7(11)-eremophilene-6,8,12-trimethoxy-3-ol, beta-obscurine, myxalamid B, momordol, and avocadyne 4-acetate) may be responsible for the observed cytotoxicity potential of H fraction of P. osteratus.

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A gas chromatography-triple quadrupole mass spectrometry(GC-MS) method was established for the simultaneous determination of eleven volatile components in Cinnamomi Oleum and the chemical pattern recognition was utilized to evaluate the quality of essential oil obtained from Cinnamomi Fructus medicinal materials in various habitats. The Cinnamomi Fructus medicinal materials were treated by water distillation, analyzed using GC-MS, and detected by selective ion monitoring(SIM), and the internal standards were used for quantification. The content results of Cinnamomi Oleum from various batches were analyzed by hierarchical clustering analysis(HCA), principal component analysis(PCA), and orthogonal partial least squares-discriminant analysis(OPLS-DA) for the statistic analysis. Eleven components showed good linear relationships within their respective concentration ranges(R~2>0.999 7), with average recoveries of 92.41%-102.1% and RSD of 1.2%-3.2%(n=6). The samples were classified into three categories by HCA and PCA, and 2-nonanone was screened as a marker of variability between batches in combination with OPLS-DA. This method is specific, sensitive, simple, and accurate, and the screened components can be utilized as a basis for the quality control of Cinnamomi Oleum.

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