AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem.

Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.

A novel comprehensive strategy for high-thoroughly studying released compounds during the combustion process of herbs. A case study for Artemisia argyi Levl. et Vant.

The comprehensive study of compound variations in released smoke during the combustion process is a great challenge in many scientific fields related to analytical chemistry like traditional Chinese medicine, environment analysis, food analysis, etc. In this work, we propose a new comprehensive strategy for efficiently and high-thoroughly characterizing compounds in the online released complex smokes: (i) A smoke capture device was designed for efficiently collecting chemical constituents to perform gas chromatography-mass spectrometry (GC-MS) based untargeted analysis. (ii) An advanced data analysis tool, AntDAS-GCMS, was used for automatically extracting compounds in the original acquired GC-MS data files. Additionally, a GC-MS data analysis guided instrumental parameter optimizing strategy was proposed for the optimization of parameters in the smoke capture device. The developed strategy was demonstrated by the study of compound variations in the smoke of traditional Chinese medicine, Artemisia argyi Levl. et Vant. The results indicated that more than 590 components showed significant differences among released smokes of various moxa velvet ratios. Finally, about 88 compounds were identified, of which phenolic compounds were the most abundant, followed by aromatics, alkenes, alcohols and furans. In conclusion, we may provide a novel approach to the studies of compounds in online released smoke.

MW-9, a chalcones derivative bearing heterocyclic moieties, ameliorates ulcerative colitis via regulating MAPK signaling pathway / 中国药理学通报

Aim To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.

Using UHPLC-HRMS-based comprehensive strategy to efficiently and accurately screen and identify illegal additives in health-care foods.

Accurately and high-thoroughly screening illegal additives in health-care foods continues to be a challenging task in routine analysis for the ultrahigh performance liquid chromatography-high resolution mass spectrometry based techniques. In this work, we proposed a new strategy to identify additives in complex food matrices, which consists of both experimental design and advanced chemometric data analysis. At first, reliable features in the analyzed samples were screened based on a simple but efficient sample weighting design, and those related to illegal additives were screened with robust statistical analysis. After the MS1 in-source fragment ion identification, both MS1 and MS/MS spectra were constructed for each underlying compound, based on which illegal additives can be precisely identified. The performance of the developed strategy was demonstrated by using mixture and synthetic sample datasets, indicating an improvement of data analysis efficiency up to 70.3 %. Finally, the developed strategy was applied for the screening of unknown additives in 21 batches of commercially available health-care foods. Results indicated that at least 80 % of false-positive results can be reduced and 4 additives were screened and confirmed.

A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics.

Data analysis of ultrahigh performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) is an essential and time-consuming step in plant metabolomics and feature extraction is the fundamental step for current tools. Various methods lead to different feature extraction results in practical applications, which may puzzle users for selecting adequate data analysis tools to deal with collected data. In this work, we provide a comprehensive method evaluation for some advanced UHPLC-HRMS data analysis tools in plant metabolomics, including MS-DIAL, XCMS, MZmine, AntDAS, Progenesis QI, and Compound Discoverer. Both mixtures of standards and various complex plant matrices were specifically designed for evaluating the performances of the involved method in analyzing both targeted and untargeted metabolomics. Results indicated that AntDAS provide the most acceptable feature extraction, compound identification, and quantification results in targeted compound analysis. Concerning the complex plant dataset, both MS-DIAL and AntDAS can provide more reliable results than the others. The method comparison is maybe useful for the selection of suitable data analysis tools for users.

A study of flavor variations during the flaxseed roasting procedure by developed real-time SPME GC-MS coupled with chemometrics.

Volatile compound variations during the roasting procedure play an essential role in the flaxseed-related product. In this work, we proposed a new strategy to high-throughput characterize the dynamic variations of flavors in flaxseed. Volatile compounds released at various roasting times were comprehensively investigated by a newly developed real-time solid-phase microextraction coupled with gas chromatography-mass spectrometry (GC-MS). Raw data files were analyzed by our advanced GC-MS data analysis software AntDAS-GCMS. Chemometric methods such as principal component analysis and partial least squares-discrimination analysis have realized the differences of samples with various roasting times. Finally, a total of 51 compounds from 11 aromas were accurately identified and confirmed with standards, and their variations as a function of roasting time were studied. In conclusion, we provided a new solution for the online monitoring of volatile compounds during the industrial roasting process.

AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification.

Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.

Framing Effects in Older Adults' Medical Decision-Making: Social Distance Matters.

OBJECTIVES: The present study sought to explore how decision making is influenced by aging, framing, and social distance in the medical domain. Based on Socioemotional Selectivity Theory, we predicted that social distance would moderate age differences of framing effects as a result of older adults' emphasis on close partners. METHODS: Younger and middle-aged (N = 206) and older (N = 208) adults from Shanghai, China completed 2 medical decision tasks in which they were presented with different descriptions of social distance, namely deciding for close relatives or for strangers. Participants' risk preferences were measured. RESULTS: The results showed that framing effects were a function of social distance in older adults. Older adults showed smaller framing effects when making decisions for strangers as their preference for the riskier option was reduced in the loss-framed condition. For younger and middle-aged adults, framing effects existed consistently regardless of social distance. DISCUSSION: These findings suggest that social distance moderates age differences in framing effects in medical decision-making. It also highlights a potential way to improve older adults' medical decision-making quality: having older adults imagine as if they are making medical decisions for a stranger.

Enzymatic Digestion Method Coupled with Artificial Intelligence Techniques in Forensic Drowning Diatom Detection / 中山大学学报(医学科学版)

ObjectiveArtificial intelligence （AI） full smear automated diatom detection technology can perform forensic pathology drowning diatom detection more quickly and efficiently than human experts.However， this technique was only used in conjunction with the strong acid digestion method， which has a low extraction rate of diatoms. In this study， we propose to use the more efficient proteinase K tissue digestion method （hereinafter referred to as enzyme digestion method） as a diatom extraction method to investigate the generalization ability and feasibility of this technique in other diatom extraction methods. MethodsLung tissues from 6 drowned cadavers were collected for proteinase K ablation and made into smears， and the smears were digitized using the digital image matrix cutting method and a diatom and background database was established accordingly.The data set was divided into training set， validation set and test set in the ratio of 3：1：1， and the convolutional neural network （CNN） models were trained， internally validated， and externally tested on the basis of ImageNet pre-training. ResultsThe results showed that the accuracy rate of the external test of the best model was 97.65 %， and the area where the model features were extracted was the area where the diatoms were located. The best CNN model in practice had a precision of more than 80 % for diatom detection of drowned corpses. ConclusionIt is shown that the AI automated diatom detection technique based on CNN model and enzymatic digestion method in combination can efficiently identify diatoms and can be used as an auxiliary method for diatom detection in drowning identification.

Mechanism of Gentisic Acid on Rheumatoid Arthritis Based on miR-19b-3p/RAF1 Axis / 中国结合医学杂志

OBJECTIVE@#To investigate the therapeutic effect of gentisic acid (GA) on rheumatoid arthritis (RA) based on the miR-19b-3p/RAF1 axis.@*METHODS@#The cell counting kit-8 method was used to detect the growth inhibitory effect of different concentrations of GA on MH7A cells, and the drug concentration of GA was determined in the experiment. The quantificational real-time polymerase chain reaction (qRT-PCR) was used to detect the expression of miR-19b-3p and RAF1. RAF1, extracellular regulated protein kinases1/2 (ERK1/2) and phospho-ERK1/2 (p-ERK1/2) were examined by Western blotting. Three methods (dual-luciferase assay, qRT-PCR and Western blot analysis) were used to verify miR-19b-3p targeting RAF1. Flow cytometry was performed to detect MH7A cell apoptosis. Transwell and wound healing assays were used to determine the invasion and migration capacities of MH7A cells.@*RESULTS@#The growth of MH7A cells was gradually inhibited with increasing GA concentration. When the GA concentration exceeded 80 mmol/L, GA was significantly cytotoxic to MH7A cells, so the half maximal inhibitory concentration of GA for MH7A cells was calculated as 67.019 mmol/L. GA upregulated miR-19b-3p expression, downregulated RAF1 expression, inhibited ERK1/2 phosphorylation, induced MH7A cell apoptosis and suppressed MH7A cell invasion and migration (P<0.05 or P<0.01). RAF1 was identified as the target of miR-19b-3p and reversed inhibitory effects on miR-19b-3p expression (P<0.05 or P<0.01). The miR-19b-3p inhibitor upregulated RAF1 expression and ERK1/2 phosphorylation, suppressed MH7A cell apoptosis and induced MH7A cell invasion and migration (P<0.01).@*CONCLUSION@#GA regulated miR-19b-3p/RAF1 axis to mediate ERK pathway and inhibit the development of RA.

A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics.

The pseudotargeted metabolomics based on gas chromatography-mass spectrometry (GC-MS) has the advantage of filtering out artifacts originating from sample treatment and accurately quantifying underlying compounds in the analyzed samples. However, this technique faces the problem of selecting high-quality selective ions for performing selected ion monitoring (SIM) on instruments. In this work, we proposed AntDAS-SIMOpt, an automatic untargeted strategy for SIM ion optimization that was accomplished on the basis of an experimental design combined with advanced chemometric algorithms. First, a group of diluted quality control samples was used to screen underlying compounds in samples automatically. Ions in each of the resolved mass spectrum were then evaluated by using the developed algorithms to identify the SIM ion. A Matlab graphical user interface (GUI) was designed to facilitate routine analysis, which can be obtained from http://www.pmdb.org.cn/antdassimopt. The performance of the developed strategy was comprehensively investigated by using standard and complex plant datasets. Results indicated that AntDAS-SIMOpt may be useful for GC-MS-based metabolomics.

A new platform for untargeted UHPLC-HRMS data analysis to address the time-shift problem.

Substantial deviations in retention times among samples pose a great challenge for the accurate screening and identifying of metabolites by ultrahigh-performance liquid chromatography high-resolution mass spectrometry (UHPLC-HRMS). In this study, a coarse-to-refined time-shift correction methodology was proposed to efficiently address this problem. Metabolites producing multiple fragment ions were automatically selected as landmarks to generate pseudo-mass spectra for a coarse time-shift correction. Refined peak alignment for extracted ion chromatograms was then performed by using a moving window-based multiple-peak alignment strategy. Based on this novel coarse-to-refined time-shift correction methodology, a new comprehensive UHPLC-HRMS data analysis platform was developed for UHPLC-HRMS-based metabolomics. Original datasets were employed as inputs to automatically extract and register features in the dataset and to distinguish fragment ions from metabolites for chemometric analysis. Its performance was further evaluated using complex datasets, and the results suggest that the new platform can satisfactorily resolve the time-shift problem and is comparable with commonly used UHPLC-HRMS data analysis tools such as XCMS Online, MS-DIAL, Mzmine2, and Progenesis QI. The new platform can be downloaded from: http://www.pmdb.org.cn/antdas2tsc.

The Bounded Ending Effect in the Domain of Football Gambling: High Risks Are Not Always Preferred at the End.

Previous research has demonstrated that individuals present increased risk taking at the end of a series of gambles, a phenomenon called the ending effect. By using a large online football gambling data, we attempted to replicate the ending effect and tested the existence of a different pattern of increased risk taking at the ending. The results showed that at the end of a series of football gambles, bettors would take more risks, and options with lower risk would be preferred. In games without the interference of local bettors, the ending effect was found to be stronger than that of games with local bettors. Overall, the results provide evidence for the bounded ending effect. Possible underlying mechanisms of the bounded ending effect are explored.

Optimization of the detection method for urine S-phenylmercapturic acid via modified performance liquid chromatography-mass spectrometry / 中国职业医学

@#Objective - To optimize the extraction and quantification methods for the determination of S phenylmercapturic acid - Methods (SPMA) in urine with performance liquid chromatography mass spectrometry. The urine was hydrolyzed with 50.0% sulfuric acid. The hydrolysate was purified by solid phase extraction column. Purified samples were separated by C18 chromatographic column and detected by tandem mass spectrometry. The isotope labeled SPMA was used as the internal Results - standard. The internal standard curve was used for quantification. The linear range of SPMA was 0.50 50.00 μg/L with the correlation coefficient of 0.999 8. The detection limit and the lower limit of quantification were 0.05 and 0.17 μg/L, - - - - respectively. The recovery rate was 97.0% 102.0%. The within run and between run relative standard deviation were 0.6% 1.0% - and 1.7% 6.5%, respectively. The mass concentration of urinary SPMA in the occupational benzene exposure group was - vs P higher than the non occupational benzene exposure group by this method (median: 2.81 0.28 μg/g creatinine, <0.05). Conclusion Compared to the national standard method, this optimized method of solid phase extraction and internal standard for quantification eliminates the matrix effect. This method is accurate and precise, and is suitable for the determination of SPMA acid in urine.

How Family-Work Conflict Influences Post-traumatic Growth Among Medical Workers: A Moderated Mediation Model.

Under the impact of COVID-19, the status and mechanisms of post-traumatic growth among medical workers facing challenges related to family-work conflict are of great concern. In view of the complex relationship between family-work conflict and post-traumatic growth, the present study sought to explore the specific relationships between family-work conflict and post-traumatic growth as well as the specific roles of positive psychological capital, perceived social support, and suppression. We recruited 1,347 participants. The results revealed that positive psychological capital and perceived social support played mediating roles, while suppression strategies moderated the mediating effect. Compared with the low suppression group, the negative impact of family-work conflict on positive psychological capital and perceived social support was reduced in the high suppression group. Thus, a higher level of suppression was more conducive to post-traumatic growth. The current study enriches and expands the findings of previous studies in theory and provides practical ways to promote post-traumatic growth in medical workers.

Longshot or Favorite: The Ending Effect in Investment Decisions.

The ending effect describes the phenomenon that at the end of a series of repeated risky decision-making tasks, participants become more likely to engage in risk-taking behavior. Past research has suggested that the ending effect might be caused by a motivational shift induced by changes in time perception. Previous studies mainly tested this phenomenon in a binary decision-making setting (e.g., a decision-making task usually includes two alternatives). However, none of these prior studies included safe options and risky options that differed in risk levels. To address this knowledge gap, the present study replicated the ending effect in a repeated decision-making task that included both a safe option and risky options that differed in risk levels (N = 104). We found that at the end of the decision-making task, participants became more likely to engage in risk-taking and to favor the option with the highest risk. Further, we found that the investment likelihood and investment amount of high-risk options both increased significantly at the ending. In addition, a shift in favoring the safe option emerged in the noninformed condition at the end. We also found that the emotional motivation in the last round could predict the increased preference for high-risk at the ending. This study extended previous findings on the ending effect by adopting a more complex decision-making scenario and, more broadly, helped further our understanding of the psychological consequences of perceived endings.

How healthcare workers respond to COVID-19: The role of vulnerability and social support in a close relationships defense mechanism.

Healthcare workers play a vital role in the fight against COVID-19. Based on Terror Management Theory (TMT), the present research examined whether a close relationships defense mechanism reduces anxiety among healthcare workers (N = 729) in China. Our results suggest that this defense mechanism, as indexed by relationship satisfaction, serves as an effective terror management source after exposure to reminders of death (MS; mortality salience). These findings extend TMT by identifying two moderating variables: vulnerability and social support. In a low objective vulnerability group, healthcare workers who subjectively believed themselves as less vulnerable to COVID-19 showed a stronger defense mechanism after a MS manipulation as compared to those who felt more vulnerable. Further, healthcare workers with higher levels of social support reported more relationship satisfaction. These findings have practical implications for guiding healthcare workers on how to buffer death-related anxiety and maintain their mental health in the fight against COVID-19.

A chemometric strategy for accurately identifying illegal additive compounds in health foods by using ultra-high-performance liquid chromatography coupled to high resolution mass spectrometry.

The accurate identification of unknown illegal additive compounds in complex health foods continues to be a challenging task in routine analysis, because massive false positive results can be screened with ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry-based untargeted techniques and must be manually filtered out. To address this problem, we developed a chemometric-based strategy, in which data analysis was first performed by using XCMS, MS-DIAL, Mzmine2, and AntDAS2, to select those that provided acceptable results to extract common features (CFs), which can be detected by all of the selected methods. Then, CFs whose contents were significantly higher in the suspected illegal additive group were screened. Isotopic, adduct, and neutral loss ions were marked based on the CFs by using a new adaptive ion annotation algorithm. Fragment ions originating from the same compound were identified by using a novel fragment ion identification algorithm. Finally, a respective mass spectrum was constructed for each screened compound to benefit compound identification. The developed strategy was confirmed by using a complex Chinese health food, Goujiya tea. The features of all illegal additive compounds were precisely screened by the developed strategy, and massive false positive features from the current data analysis method were greatly reduced. The constructed respective mass spectra can benefit compound identification and avoid the risk of identifying ions from the same illegal compound as different compounds. Moreover, unknown compounds that are contained in an illegal compound library can be screened.

Effects of miRNA-140 on the Growth and Clinical Prognosis of SMMC-7721 Hepatocellular Carcinoma Cell Line.

BACKGROUND: A growing number of studies have suggested that microRNAs exert an essential role in the development and occurrence of multiple tumours and act as crucial regulators in various biological processes. However, the expression and function of miRNA-140 in hepatocellular carcinoma (HCC) cells are not yet adequately identified and manifested. METHODS: The expression of miRNA-140 was determined in HCC tissues and adjacent nontumour tissues by quantitative real-time polymerase chain reaction (qRT-PCR). Kaplan-Meier survival analysis and Cox regression analysis were performed to explore the correlation between miRNA-140 expression level and the survival rate of patients with HCC. Additionally, overexpression experiments were conducted to investigate the biological role of miRNA-140 in HCC cells. Bioinformatics was used to predict the related target genes and pathways of miRNA-140. RESULTS: QRT-PCR results signified that the expression level of miRNA-140 in HCC was lower than that of adjacent normal tissues (P < 0.0001). Compared with the control group, the SMMC-7721 HCC cells in the miRNA-140 mimic group had a decrease in proliferation, migration, and invasion (P < 0.05), whereas those in the miRNA-140 inhibitor group had an increase in proliferation, migration, and invasion (P < 0.05). Cell cycle arrest occurred in the G0/1 phase. Prognosis analysis showed that the expression level of miRNA-140 was not related to the prognosis of HCC. Furthermore, the Kaplan-Meier test revealed that patients with lower miRNA-140 expression levels in liver cancer tissue had significantly shorter disease-free survival (DFS, P = 0.004) and overall survival (OS) times (P = 0.010) after hepatectomy. Cox regression analysis further indicated that miRNA-140 was an independent risk factor that may affect the DFS (P = 0.004) and OS times (P = 0.014) of patients after hepatectomy. Our results suggested that miRNA-140 might be a crucial regulator involved in the HCC progression and is thus considered a potential prognostic biomarker and therapeutic target for HCC.