Variational theory of angulons and their rotational spectroscopy.

The angulon, a quasiparticle formed by a quantum rotor dressed by the excitations of a many-body bath, can be used to describe an impurity rotating in a fluid or solid environment. Here, we propose a coherent state ansatz in the co-rotating frame, which provides a comprehensive theoretical description of angulons. We reveal the quasiparticle properties, such as energies, quasiparticle weights, and spectral functions, and show that our ansatz yields a persistent decrease in the impurity's rotational constant due to many-body dressing, which is consistent with experimental observations. From our study, a picture of the angulon emerges as an effective spin interacting with a magnetic field that is self-consistently generated by the molecule's rotation. Moreover, we discuss rotational spectroscopy, which focuses on the response of rotating molecules to a laser perturbation in the linear response regime. Importantly, we take into account initial-state interactions that have been neglected in prior studies and reveal their impact on the excitation spectrum. To examine the angulon instability regime, we use a single-excitation ansatz and obtain results consistent with experiments, in which a broadening of spectral lines is observed while phonon wings remain highly suppressed due to initial-state interactions.

Screening out Biomarkers of Tetrastigma hemsleyanum for Anti-Cancer and Anti-Inflammatory Based on Spectrum-Effect Relationship Coupled with UPLC-Q-TOF-MS.

Tetrastigma hemsleyanum Diels et Gilg. (T. hemsleyanum) is an economically and medicinally valuable species within the genus Tetrastigma. However, the material basis of its pharmacological action and the biomarkers associated with its anti-cancer and anti-inflammatory effects are still unclear. Additionally, the T. hemsleyanum industry cannot grow because there is a lack of a scientific, universal, and measurable quality control system. This study aimed to explore the chemical basis quality markers related to the anti-cancer and anti-inflammatory effects of T. hemsleyanum to establish an effective quality evaluation method. UPLC-Q-TOF-MSE fingerprint profiles of T. hemsleyanum from different origins were established. Pharmacodynamic studies used HepG2 and HuH-7 cells and LPS-induced RAW264.7 to evaluate the anti-tumor and anti-inflammatory effects of the active ingredients. The spectrum-effect relationships between UPLC fingerprints and anti-cancer and anti-inflammatory activities were evaluated using PCA and PLSR statistical methods. Moreover, docking analysis was performed to identify specific active biomarkers with molecular targets associated with cancer and inflammation. Chlorogenic acid, quinic acid, catechin, kaempferol 3-rutinoside, apigenin-8-C-glucoside, and linolenic acid were associated with anticancer activity, while chlorogenic acid, quercetin, quinic acid, kaempferol 3-rutinoside, rutinum, apigenin-8-C-glucoside, and linolenic acid were associated with anti-inflammatory activity. The spectrum-effect relationship of T. hemsleyanum was successfully established, and the biomarkers for anti-cancer and anti-inflammatory effects were preliminary confirmed. These findings provide a theoretical basis for the elucidation of the substance basis of T. hemsleyanum and lay the foundation for its rapid identification, quality control, industrial research, and utilization.

Information Entropy-Based Strategy for the Quantitative Evaluation of Extensive Hyperspectral Images to Better Unveil Spatial Heterogeneity in Mass Spectrometry Imaging.

Hyperspectral images can be generated from mass spectrometry imaging (MSI) data for the intuitive data visualization purpose. However, hundreds of HSIs can be generated by different dimensionality reduction methods, which poses great challenges in selecting the high-quality images with the best intuitive visualization results of the MSI data. Here, we presented a novel approach that objectively evaluates the image quality of the hyperspectral images. The applicability of this method was demonstrated by analyzing the MSI data acquired from human prostate cancer biopsy samples and mouse brain tissue section, which harbored an intrinsic tissue heterogeneity. Our method was based on the information entropy and contrast measured from image information content and image definition, respectively. The heterogeneity of the MSI data from high-dimensional space was reduced to three-dimensional embeddings and thoroughly evaluated to achieve satisfactory visualization results. The application of information entropy and contrast can be used to choose the optimized visualization results rapidly and objectively from an extensive number of hyperspectral images and be adopted to evaluate and optimize different dimensionality reduction algorithms and their hyperparameter combinations. In conclusion, the information entropy-based strategy could be a bridge between chemometrician and biologists.

Ion-Pair Selection Method for Pseudotargeted Metabolomics Based on SWATH MS Acquisition and Its Application in Differential Metabolite Discovery of Type 2 Diabetes.

The pseudotargeted metabolomics method integrates advantages of nontargeted and targeted analysis because it can acquire data of metabolites in the multireaction monitoring (MRM) mode of mass spectrometry (MS) without needing standards. The key is the ion-pair information collection from samples to be analyzed. It is well-known that sequential windowed acquisition of all theoretical Fragment ion (SWATH) MS mode can acquire MS2 information to a maximum extent. To expediently acquire as many ion-pairs as possible with optimal collision energy (CE), an ion-pair selection approach based on SWATH MS acquisition with variable isolation windows was developed in this study. Initially, nontargeted acquisition of all metabolites information in plasma Standard Reference Material (SRM 1950) was performed by ultra high-performance liquid chromatography (UHPLC)-quadrupole time-of-flight (Q-TOF) MS platform with three CEs. With the help of software tool, the ion-pairs of unique metabolites were gained. Then they were validated in scheduled MRM coupled with UHPLC. After removing false positive, the ion-pairs with an optimal CE was integrated. A total of 1373 unique metabolite ion-pairs were obtained at positive ion mode. And repeatability of the established pseudotargeted approach was evaluated by intraday and interday precision. The results demonstrated the method was stable, reliable, and suitable for metabolomics study. As an application example, alterations of serum metabolites in Type 2 diabetes were investigated by using the established method. This work provides a pseudotargeted ion-pair selection method based on SWATH MS acquisition with the characters of increased metabolite coverage, suitable CE, and convenient processing.

Comprehensive Strategy to Construct In-House Database for Accurate and Batch Identification of Small Molecular Metabolites.

Identification of the metabolites is an essential step in metabolomics study to interpret the regulatory mechanism of pathological and physiological processes. However, it is still difficult in LC-MS n-based studies because of the complexity of mass spectrometry, chemical diversity of metabolites, and deficiency of standards database. In this work, a comprehensive strategy is developed for accurate and batch metabolite identification in nontargeted metabolomics studies. First, a well-defined procedure was applied to generate reliable and standard LC-MS2 data, including tR, MS1, and MS2 information at a standard operational procedure. An in-house database including about 2000 metabolites was constructed and used to identify the metabolites in nontargeted metabolic profiling by retention time calibration using internal standards, precursor ion alignment and ion fusion, auto-MS2 information extraction and selection, and database batch searching and scoring. As an application example, a pooled serum sample was analyzed to deliver the strategy, and 202 metabolites were identified in the positive ion mode. It shows our strategy is useful for LC-MS n-based nontargeted metabolomics study.

A Novel Strategy for Large-Scale Metabolomics Study by Calibrating Gross and Systematic Errors in Gas Chromatography-Mass Spectrometry.

Metabolomics is increasingly applied to discover and validate metabolite biomarkers and illuminate biological variations. Combination of multiple analytical batches in large-scale and long-term metabolomics is commonly utilized to generate robust metabolomics data, but gross and systematic errors are often observed. The appropriate calibration methods are required before statistical analyses. Here, we develop a novel correction strategy for large-scale and long-term metabolomics study, which could integrate metabolomics data from multiple batches and different instruments by calibrating gross and systematic errors. The gross error calibration method applied various statistical and fitting models of the feature ratios between two adjacent quality control (QC) samples to screen and calibrate outlier variables. Virtual QC of each sample was produced by a linear fitting model of the feature intensities between two neighboring QCs to obtain a correction factor and remove the systematic bias. The suggested method was applied to handle metabolic profiling data of 1197 plant samples in nine batches analyzed by two gas chromatography-mass spectrometry instruments. The method was evaluated by the relative standard deviations of all the detected peaks, the average Pearson correlation coefficients, and Euclidean distance of QCs and non-QC replicates. The results showed the established approach outperforms the commonly used internal standard correction and total intensity signal correction methods, it could be used to integrate the metabolomics data from multiple analytical batches and instruments, and it allows the frequency of QC to one injection of every 20 real samples. The suggested method makes a large amount of metabolomics analysis practicable.

The development of plasma pseudotargeted GC-MS metabolic profiling and its application in bladder cancer.

Bladder cancer (BC) is a fatal malignancy with considerable mortality. BC urinary metabolomics has been extensively investigated for biomarker discovery, but few BC blood metabolomic studies have been performed. Hence, a plasma pseudotargeted metabolomic method based on gas chromatography-mass spectrometry with selected ion monitoring (GC-MS-SIM) was developed to study metabolic alterations in BC. The analytical performance of the developed method was compared with that of a nontargeted method. The relative standard deviation (RSD) values of 89 and 70.7 % of the peaks obtained using the pseudotargeted and nontargeted methods, respectively, were less than 20 %. The Pearson correlations of 90.7 and 78.3 % of the peaks obtained using the pseudotargeted and nontargeted methods, respectively, exceeded 0.90 in the linearity evaluation. Compared with the nontargeted method, the signal-to-noise ratios (S/N) of 97.9 and 69.3 % of the peaks increased two- and fivefold, respectively. The developed method was fully validated, with good precision, recovery, and stability of the trimethylsilyl (TMS) derivatives. The method was applied to investigate BC. Significant increases in the contents of metabolites involved in, for example, the pentose phosphate pathway (PPP) and nucleotide and fatty acid synthesis were found in the high-grade (HG) BC group compared to the healthy control (HC) group. These differences imply that the activated PPP may regulate BC cell proliferation by promoting lipid and nucleotide biosynthesis and the detoxification of reactive oxygen species (ROS). These results illustrate that the plasma pseudotargeted method is a powerful tool for metabolic profiling. Graphical abstract The plasma pseudotargeted metabolic profiling suggested the metabolic alterations in bladder cancer (BC) and the significantly differential metabolites for BC discrimination.

Sample-directed pseudotargeted method for the metabolic profiling analysis of rice seeds based on liquid chromatography with mass spectrometry.

Rice is one of the most important food crops in the world. Metabolite composition in rice seeds varies significantly depending on genetic variety, climatic alternation and agricultural practice. Metabolomics is a powerful tool to reveal the metabolic response of rice to various conditions. In this work, a rice seed sample-directed pseudotargeted metabolomics method was first established and validated based on ultra high performance liquid chromatography with triple quadrupole mass spectrometry in the multiple reaction monitoring mode. A total of 749 and 617 ion pairs in positive and negative modes were achieved, respectively. Among them, about 200 metabolites were identified or tentatively identified. The developed method showed better linearity and repeatability than those of non-targeted metabolomics method. Good intra-day and inter-day precisions, recoveries and wide linear range were also obtained. Furthermore, the method was applied for the investigation of metabolic variation of rice seeds with two wild cultivars and their transgenic lines that were grown in two locations. Principal component analysis indicated that the effects of cultivar and location on metabolic variations were far more than those of gene modification. The nonparametric Mann-Whitney U test revealed that most metabolites were influenced by cultivar, location and gene modifications together.

Multiple reaction monitoring-ion pair finder: a systematic approach to transform nontargeted mode to pseudotargeted mode for metabolomics study based on liquid chromatography-mass spectrometry.

Pseudotargeted metabolic profiling is a novel strategy combining the advantages of both targeted and untargeted methods. The strategy obtains metabolites and their product ions from quadrupole time-of-flight (Q-TOF) MS by information-dependent acquisition (IDA) and then picks targeted ion pairs and measures them on a triple-quadrupole MS by multiple reaction monitoring (MRM). The picking of ion pairs from thousands of candidates is the most time-consuming step of the pseudotargeted strategy. Herein, a systematic and automated approach and software (MRM-Ion Pair Finder) were developed to acquire characteristic MRM ion pairs by precursor ions alignment, MS(2) spectrum extraction and reduction, characteristic product ion selection, and ion fusion. To test the reliability of the approach, a mixture of 15 metabolite standards was first analyzed; the representative ion pairs were correctly picked out. Then, pooled serum samples were further studied, and the results were confirmed by the manual selection. Finally, a comparison with a commercial peak alignment software was performed, and a good characteristic ion coverage of metabolites was obtained. As a proof of concept, the proposed approach was applied to a metabolomics study of liver cancer; 854 metabolite ion pairs were defined in the positive ion mode from serum. Our approach provides a high throughput method which is reliable to acquire MRM ion pairs for pseudotargeted metabolomics with improved metabolite coverage and facilitate more reliable biomarkers discoveries.

Study of induction chemotherapy efficacy in oral squamous cell carcinoma using pseudotargeted metabolomics.

The effect of induction chemotherapy on oral cancer is controversial owing to inconsistent results. However, the efficacy of induction chemotherapy is closely related to locoregional recurrence, distant metastasis, and overall survival after the treatment. A pseudotargeted metabolomics revealed that metabolites involved in glycolysis and amino acid metabolism were inversely regulated in patients with different chemotherapy responses, and most fatty acids, steroids, and antioxidant substances were up-regulated in all patients after the treatment. Among the metabolites, lactic acid, glucose, glutamic acid, aspartic acid, leucine, and glycerol were remarkably associated with induction chemotherapy efficacy. Subsequently, lactic acid, glutamic acid, and aspartic acid were defined as potential biomarkers of the suitability and efficacy of induction chemotherapy. Our results show that 100.0 and 84.37% of patients with different chemotherapy efficacy were correctly identified in the training and test sets, respectively. Moreover, patient suitability for treatment was correctly predicted for 100.0, 81.25, and 100.0% of patients in the training, test, and external validation sets, respectively. In conclusion, metabolites related to glycolysis, redox homeostasis, and anabolic progress were indicative of induction chemotherapy efficacy both pre- and post-chemotherapy and beneficial for outcome evaluation and prediction. These results illustrate the potentials of metabolomics in personalized induction chemotherapy.

Ion fusion of high-resolution LC-MS-based metabolomics data to discover more reliable biomarkers.

A systematic approach for the fusion of associated ions from a common molecule was developed to generate "one feature for one peak" metabolomics data. This approach guarantees that each molecule is equally selected as a potential biomarker and may largely enhance the chance to obtain reliable findings without employing redundant ion information. The ion fusion is based on low mass variation in contrast to the theoretical calculation measured by a high-resolution mass spectrometer, such as LTQ orbitrap, and a high correlation of ion pairs from the same molecule. The mass characteristics of isotopic distribution, neutral loss, and adduct ions were simultaneously applied to inspect each extracted ion in the range of a predefined retention time window. The correlation coefficient was computed with the corresponding intensities of each ion pair among all experimental samples. Serum metabolomics data for the investigation of hepatocellular carcinoma (HCC) and healthy controls were utilized as an example to demonstrate this strategy. In total, 609 and 1084 ion pairs were respectively found meeting one or more criteria for fusion, and therefore fused to 106 and 169 metabolite features of the datasets in the positive and negative modes, respectively. The important metabolite features were separately discovered and compared to distinguish the HCC from the healthy controls using the two datasets with and without ion fusion. The results show that the developed method can be an effective tool to process high-resolution mass spectrometry data in "omics" studies.

Nontargeted modification-specific metabolomics study based on liquid chromatography-high-resolution mass spectrometry.

Modifications of genes and proteins have been extensively studied in systems biology using comprehensive analytical strategies. Although metabolites are frequently modified, these modifications have not been studied using -omics approaches. Here a general strategy for the nontargeted profiling of modified metabolites, which we call "nontargeted modification-specific metabolomics", is reported. A key aspect of this strategy was the combination of in-source collision-induced dissociation liquid chromatography-mass spectrometry (LC-MS) and global nontargeted LC-MS-based metabolomics. Characteristic neutral loss fragments that are specific for acetylation, sulfation, glucuronidation, glucosidation, or ribose conjugation were reproducibly detected using human urine as a model specimen for method development. The practical application of this method was demonstrated by profiling urine samples from liver cirrhosis patients. Approximately 900 features were identified as modified endogenous metabolites and xenobiotics. Moreover, this strategy supports the identification of compounds not included in traditional metabolomics databases (HMDB, Metlin, and KEGG), which are currently referred to as "unknowns" in metabolomics projects. Nontargeted modification-specific metabolomics opens a new perspective in systems biology.

Study of metabolite differences of flue-cured tobacco from different regions using a pseudotargeted gas chromatography with mass spectrometry selected-ion monitoring method.

A pseudotargeted method based on gas chromatography and mass spectrometry with selected-ion monitoring was established to investigate the metabolite differences of flue-cured tobacco from three different growing regions. The mixed solvent of acetonitrile/isopropanol/water (3:3:2, v/v/v) was chosen as the optimal extraction system based on the good repeatability and extraction efficiency. A self-developed software coupled with commercial software was used to establish the pseudotargeted method including 289 peaks and 47 groups. Multivariable statistical analysis indicated that tobacco samples can be obviously separated based on the geographical origins. On the basis of a Mann-Whitney U test, organic acids, phenols, and alkaloids had higher levels in Hunan province. In contrast, a large proportion of amino acids (including L-tyrosine, L-proline, and serine), sucrose, and linoleic acid were the highest in Yunnan province. Meanwhile, multiple metabolic pathways (including carbohydrate metabolism, tricarboxylic acid cycle, and nitrogen metabolism) were influenced by growing regions. Twenty-eight differential metabolites, which had great contributions to the classification of tobacco samples of three growing regions, were further defined. The results demonstrated that the developed pseudotargeted method was a powerful tool to investigate the metabolic profiling of tobacco leaves and discriminate tobacco leaves of different growing regions.

Study on the correlation and difference of qualitative information among three types of UPLC-HRMS and potential generalization in metabolites annotation.

The variation of qualitative information among different types of mainstream hyphenated instruments of ultra-performance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) makes data sharing and standardization, and further comparison of results consistency in metabolite annotation not easy to attain. In this work, a quantitative study of correlation and difference was first achieved to systematically investigate the variation of retention time (tR), precursor ion (MS1), and product fragment ions (MS2) generated by three typical UPLC-HRMS instruments commonly used in metabolomics area. In terms of the findings of systematic and correlated variation of tR, MS1, and MS2 between different instruments, a computational strategy for integrated metabolite annotation was proposed to reduce the influence of differential ions, which made full use of the characteristic (common) and non-common fragments for scoring assessment. The regular variations of MS2 among three instruments under four collision energy voltages of high, medium, low, and hybrid levels were respectively inspected with three technical replicates at each level. These discoveries could improve general metabolite annotation with a known database and similarity comparison. It should provide the potential for metabolite annotation to generalize qualitative information obtained under different experimental conditions or using instruments from various manufacturers, which is still a big headache in untargeted metabolomics. The mixture of standard compounds and serum samples with the addition of standards were applied to demonstrate the principle and performance of the proposed method. The results showed that it could be an optional strategy for general use in HRMS-based metabolomics to offset the difference in metabolite annotation. It has some potential in untargeted metabolomics.

A modeling approach for orthogonality of comprehensive two-dimensional separations.

A novel method is developed for orthogonality evaluation of comprehensive two-dimensional separations (C2DS). Utilization of efficiency measures such as peak capacity (n(c)) can be critically evaluated for C2DS analysis to describe an orthogonal separation of the analytes in a 2D plane. Unlike most previous methods focusing on "bin coverage" over 2D space, rather than taking into account the distribution based on accurate peak retention, in the proposed method, the separation orthogonality of C2DS is divided into two parts (i.e., C(pert) and C(peaks)). These correspond to peak coverage percent, and 2D distribution correlation of compounds, respectively. Bin occupation and a simple-linear regression model, on the basis of normalized retention times in 2D separation space ((1)t(R) and (2)t(R)), are further introduced to quantitatively define the two terms. Orthogonality ranges from 0 to 1 correspond to perfectly correlated and orthogonal separations, respectively, which are presented based on both C(pert) and C(peaks) considerations. The advantage of this method is the use of separation properties of C2DS to characterize practical 2D peak distribution and does not rely on assumptions or any imposed limitations. Simulation of comprehensive two-dimensional gas chromatography (GC × GC) was achieved by using the Abraham solvation parameter model, and applied to generate examples for orthogonality assessment. In this work, 225 compounds comprising a range of chemical classes were simulated for separation on two column set pairs comprising low polarity/polar and moderately polar/polar combinations. Results illustrate that the proposed method applied to GC × GC provides a reasonable assessment of 2D separation performance and may be used to derive optimal experimental conditions when used with an experimental design strategy.

Pseudotargeted metabolomics method and its application in serum biomarker discovery for hepatocellular carcinoma based on ultra high-performance liquid chromatography/triple quadrupole mass spectrometry.

Untargeted analysis performed using full-scan mass spectrometry (MS) coupled with liquid chromatography (LC) is commonly used in metabolomics. Although they are commonly employed, full-scan MS methods such as quadrupole-time-of-flight (Q-TOF) MS have been restricted by various factors including their limited linear range and complicated data processing. LC coupled with triple quadrupole (QQQ) MS operated in the multiple reaction monitoring (MRM) mode is the gold standard for metabolite quantification; however, only known metabolites are generally quantified, limiting its applications in metabolomic analysis. In this study, a pseudotargeted approach was proposed to perform serum metabolomic analysis using an ultra high-performance liquid chromatography (UHPLC)/QQQ MS system operated in the MRM mode, for which the MRM ion pairs were acquired from the serum samples through untargeted tandem MS using UHPLC/Q-TOF MS. The UHPLC/QQQ MRM MS-based pseudotargeted method displayed better repeatability and wider linear range than the traditional UHPLC/Q-TOF MS-based untargeted metabolomics method, and no complicated peak alignment was required. The developed method was applied to discover serum biomarkers for patients with hepatocellular carcinoma (HCC). Patients with HCC had decreased lysophosphatidylcholine, increased long-chain and decreased medium-chain acylcarnitines, and increased aromatic and decreased branched-chain amino acid levels compared to healthy controls. The novelty of this work is that it provides an approach to acquire MRM ion pairs from real samples, is not limited to metabolite standards, and it provides a foundation to achieve pseudotargeted metabolomic analysis on the widely used LC/QQQ MS platform.

Stable isotope-assisted lipidomics combined with nontargeted isotopomer filtering, a tool to unravel the complex dynamics of lipid metabolism.

Investigations of complex metabolic mechanisms and networks have become a focus of research in the postgenomic area, thereby creating an increasing demand for sophisticated analytical approaches. One such tool is lipidomics analysis that provides, a detailed picture of the lipid composition of a system at a given time. Introducing stable isotopes into the studied system can additionally provide information on the synthesis, transformation and degradation of individual lipid species. However, capturing the entire dynamics of lipid networks is still a challenge. We developed and evaluated a novel strategy for the in-depth analysis of the dynamics of lipid metabolism with the capacity for high molecular specificity and network coverage. The general workflow consists of stable isotope-labeling experiments, ultrahigh-performance liquid chromatography (UHPLC)/high-resolution Orbitrap-mass spectrometry (MS) lipid profiling and data processing by a software tool for global isotopomer filtering and matching. As a proof of concept, this approach was applied to the network-wide mapping of dynamic lipid metabolism in primary human skeletal muscle cells cultured for 4, 12, and 24 h with [U-(13)C]-palmitate. In the myocellular lipid extracts, 692 isotopomers were detected that could be assigned to 203 labeled lipid species spanning 12 lipid (sub)classes. Interestingly, some lipid classes showed high turnover rates but stable total amounts while the amount of others increased in the course of palmitate treatment. The novel strategy presented here has the potential to open new detailed insights into the dynamics of lipid metabolism that may lead to a better understanding of physiological mechanisms and metabolic perturbations.

Simultaneous estimation of retention times of overlapping primary peaks in comprehensive two-dimensional GC.

A general approach is developed to estimate the retention time (t(R)) of overlapping primary peaks in comprehensive 2D GC without assumptions such as the limitation of modulation ratio or symmetry of the target primary peak. Accurate determination of t(R) can recover the original peak profile by using mathematical fitting. First, the modulation pattern of the first-dimension peak in the second dimension is summarized with the number of major modulation fractions equal to or less than four. A novel formula to derive t(R) is defined and separately investigated for cases of modulation number for 1-4, on the basis of the center of gravity of all fractions of the primary peak, in the second dimension. A moving-window search strategy is further developed for peak clusters overlapping in both separation dimensions, to extract first dimension tR of pure components with the same second dimension time after data pretreatment (background subtraction; time shift correction). Results are very close to the times of simulated comprehensive 2D GC separations. Compared to methods such as reconstruction of original chromatographic profiles to derive t(R), the proposed approach balances the precision, and ease of adaption to real applications.

Study on the Common Molecular Mechanism of Metabolic Acidosis and Myocardial Damage Complicated by Neonatal Pneumonia.

Pneumonia is a common clinical disease in the neonatal period and poses a serious risk to infant health. Therefore, the understanding of molecular mechanisms is of great importance for the development of methods for the rapid and accurate identification, classification and staging, and even disease diagnosis and therapy of pneumonia. In this study, a nontargeted metabonomic method was developed and applied for the analysis of serum samples collected from 20 cases in the pneumonia control group (PN) and 20 and 10 cases of pneumonia patients with metabolic acidosis (MA) and myocardial damage (MD), respectively, with the help of ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UPLC-HRMS). The results showed that compared with the pneumonia group, 23 and 21 differential metabolites were identified in pneumonia with two complications. They showed high sensitivity and specificity, with the area under the curve (ROC) of the receiver operating characteristic curve (ROC) larger than 0.7 for each differential molecule. There were 14 metabolites and three metabolic pathways of sphingolipid metabolism, porphyrin and chlorophyll metabolism, and glycerophospholipid metabolism existing in both groups of PN and MA, and PN and MD, all involving significant changes in pathways closely related to amino acid metabolism disorders, abnormal cell apoptosis, and inflammatory responses. These findings of molecular mechanisms should help a lot to fully understand and even treat the complications of pneumonia in infants.

Rapid authentication of variants of Gastrodia elata Blume using near-infrared spectroscopy combined with chemometric methods.

The variety is one of the most important factors to generate difference of chemical compositions, which unavoidably influences the quality of natural medicine. Thus, simple and rapid authentication of different variants has great academic and practical significance. In this study, the goal was achieved with the help of near infrared spectroscopy (NIR) and chemometrics by using Gastrodia elata Blume as an example. A total of 540 samples including two classes of variants and their forms were investigated as a whole. The mean spectra of samples of each class and their 2-D synchronous correlation spectra were simultaneously applied to discover the difference of chemical characteristics. After hybrid pre-processing of the first and second derivative combined with Savitzky-Golay and Norris filtering, partial least squares discrimination analysis (PLS-DA) on the basis of latent variable projection was used to assess the feasibility for classification. The results show higher prediction accuracy in both internal test set and external prediction set. In order to further improve the robustness for modeling, three methods for wavelength selection were comprehensively compared to optimize PLS-DA models, including variable importance in the projection (VIP), random frog (RF), and Monte Carlo uninformative variable elimination (MC-UVE). The prediction accuracy of combination of the 2nd derivative, Norris, MC-UVE and PLS-DA achieved to 99.11% and 98.89% corresponding to the internal test set and external prediction set, respectively. The strategies proposed in this work perform effectiveness for rapid and accurate authentication of variants of plants with high chemical complexity.