Summary of Study ST002082

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001322. The data can be accessed directly via it's Project DOI: 10.21228/M8TT44 This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST002082
Study TitlePredicting dying: a study of the metabolic changes and the dying process in patients with lung cancer
Study TypeObservational study
Study SummaryBackground: Accurately recognising that a person may be dying is central for improving their experience of care. Yet recognising dying is difficult and predicting dying frequently inaccurate. Methods: Serial urine samples from patients (n=112) with lung cancer were analysed using high resolution untargeted mass spectrometry. ANOVA and volcano plot analysis demonstrated metabolites that changed in the last weeks of life. Further analysis identified potential biological pathways affected. Cox lasso logistic regression was engaged to develop a multivariable model predicting the probability of survival within the last 30 days of life. Results: In total 124 metabolites changed. ANOVA analysis identified 93 metabolites and volcano plot analysis 85 metabolites. 53 metabolites changed using both approaches. Pathways altered in the last weeks included those associated with decreased oral intake, muscle loss, decreased RNA and protein synthesis, mitochondrial dysfunction, disrupted β-oxidation and one carbon metabolism. Epinephrine and cortisol increased in the last 2 weeks and week respectively. A model predicting time to death using 7 metabolites had excellent accuracy (AUC= 0.86 at day 30, 0.88 at day 20 and 0.85 at day 10) and enabled classification of patients at low, medium and high risk of dying on a Kaplan-Meier survival curve. Conclusions: Metabolomic analysis identified metabolites and their associated pathways that change in the last weeks and days of life in patients with lung cancer. Prognostic tests based on the metabolites identified have the potential to change clinical practice and improve the care of dying patients.
Institute
University of Liverpool Institute of Life Course & Medical Sciences
Last NameNorman
First NameBrendan
AddressWilliam Henry Duncan Building, 6 West Derby Street, Liverpool, UK. L7 8TX
Emailbnorman@liverpool.ac.uk
Phone(+44)151 794 9064
Submit Date2022-01-24
Num Groups6
Total Subjects112
Num Males67
Num Females45
Raw Data AvailableYes
Raw Data File Type(s)d
Analysis Type DetailLC-MS
Release Date2022-02-24
Release Version1
Brendan Norman Brendan Norman
https://dx.doi.org/10.21228/M8TT44
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

Select appropriate tab below to view additional metadata details:


Project:

Project ID:PR001322
Project DOI:doi: 10.21228/M8TT44
Project Title:Predicting dying: a study of the metabolic changes and the dying process in patients with lung cancer
Project Summary:Background: Accurately recognising that a person may be dying is central for improving their experience of care. Yet recognising dying is difficult and predicting dying frequently inaccurate. Methods: Serial urine samples from patients (n=112) with lung cancer were analysed using high resolution untargeted mass spectrometry. ANOVA and volcano plot analysis demonstrated metabolites that changed in the last weeks of life. Further analysis identified potential biological pathways affected. Cox lasso logistic regression was engaged to develop a multivariable model predicting the probability of survival within the last 30 days of life. Results: In total 124 metabolites changed. ANOVA analysis identified 93 metabolites and volcano plot analysis 85 metabolites. 53 metabolites changed using both approaches. Pathways altered in the last weeks included those associated with decreased oral intake, muscle loss, decreased RNA and protein synthesis, mitochondrial dysfunction, disrupted β-oxidation and one carbon metabolism. Epinephrine and cortisol increased in the last 2 weeks and week respectively. A model predicting time to death using 7 metabolites had excellent accuracy (AUC= 0.86 at day 30, 0.88 at day 20 and 0.85 at day 10) and enabled classification of patients at low, medium and high risk of dying on a Kaplan-Meier survival curve. Conclusions: Metabolomic analysis identified metabolites and their associated pathways that change in the last weeks and days of life in patients with lung cancer. Prognostic tests based on the metabolites identified have the potential to change clinical practice and improve the care of dying patients.
Institute:University of Liverpool Institute of Life Course Medical Sciences
Last Name:Norman
First Name:Brendan
Address:William Henry Duncan Building, 6 West Derby Street, Liverpool, Merseyside, L7 8TX, United Kingdom
Email:bnorman@liv.ac.uk
Phone:(+44)151 794 9064
Funding Source:This research received a Wellcome Trust Seed award for Science (202022/Z/16/Z) and a North West Cancer Research award (SI2018.11)
Contributors:Séamus Coyle, Elinor Chapman, David Hughes, James Baker, Andrew S Davison, Brendan P Norman, Amara Callistus Nwosu, Mark Boyd, Catriona R Mayland, Stephen Mason, John Ellershaw, Chris Probert

Subject:

Subject ID:SU002166
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Age Or Age Range:47-89 years
Gender:Male and female
Human Inclusion Criteria:Patients with lung cancer

Factors:

Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)

mb_sample_id local_sample_id Time before death (weeks)
SA198186D0D1_WHI-011-106_2018-07-171
SA198187D0D1_WHI-003_2018-04-161
SA198188D0D1_WHI-102_2018-04-251
SA198189D2D16_A-57_2017-08-221
SA198190D2D16_MC-11_2018-07-011
SA198191D0D1_WHI-001_2018-03-141
SA198192D0D1_R-32_2018-07-131
SA198193D0D1_C-04_2017-08-101
SA198194D0D1_MC-05_2017-08-161
SA198195D0D1_R-26_2018-04-201
SA198196D0D1_R-29_2018-06-011
SA198197D2D16_R-10_2016-09-151
SA198198D2D16_WHI-015-107_2018-07-201
SA198199G2_PS-R-09_01-11-20161
SA198200G2_PS-R-21_15-11-20171
SA198201G2_PS-WHI-027_04-09-20181
SA198202G3_PS-WHI-101_11-04-20181
SA198203G2_PS-MC-02_03-07-20171
SA198204G1_PS-WHI-007_11-04-20181
SA198205G1_PS-A-05_03-03-20171
SA198206G1_PS-A-51_21-11-20171
SA198207G1_PS-MC-06_14-08-20171
SA198208G1_PS-R-02_11-10-20161
SA198209D0D1_A-63_2018-03-281
SA198210G1_PS-WHI-020_15-06-20181
SA198211D0D1_A-09_2017-11-211
SA198212A-20_2017-11-2112+
SA198213A-01_2017-02-0912+
SA198214LCC_PS-MC-09_19-10-201712+
SA198215A-21_2018-01-2412+
SA198216LCC_PS-C-12_01-06-201712+
SA198217A-24_2017-07-0612+
SA198218LCC_PS-MC-14_27-02-201812+
SA198219A-40_2018-07-2412+
SA198220A-33_2017-12-1212+
SA198221A-15_2018-06-0612+
SA198222LCC_PS-C-01_09-03-201712+
SA198223LCC_PS-A-13_06-07-201712+
SA198224LCC_PS-A-08_26-07-201712+
SA198225LCC_PS-A-07_02-03-201712+
SA198226LCC_PS-A-06_20-07-201712+
SA198227LCC_PS-A-19_06-07-201712+
SA198228A-11_2018-05-1712+
SA198229LCC_PS-R-15_31-03-201712+
SA198230LCC_PS-A-50_29-06-201712+
SA198231LCC_PS-A-32_06-07-201712+
SA198232A-12_2017-03-1612+
SA198233LCC_PS-R-22_08-12-201712+
SA198234C-03_2017-03-2012+
SA198235C-02_2017-03-1612+
SA198236A-61_2018-03-1512+
SA198237WHI-016_2018-08-3012+
SA198238C-05_2017-04-2012+
SA198239C-06_2017-04-2612+
SA198240C-18_2017-06-2112+
SA198241C-13_2017-06-0612+
SA198242C-09_2017-05-1012+
SA198243R-30_2018-06-0712+
SA198244D17_A-65_2018-07-0712+
SA198245A-48_2017-06-2912+
SA198246A-47_2017-08-0212+
SA198247LCC_PS-WHI-002_14-03-201812+
SA198248LCC_PS-R-24_11-01-201812+
SA198249A-49_2017-06-2812+
SA198250LCC_PS-WHI-008_16-04-201812+
SA198251MC-17_2018-09-1212+
SA198252MC-16_2018-09-1212+
SA198253MC-15_2018-09-1912+
SA198254Group 4_PS-R-18_20-06-20172
SA198255R-13_2018-04-232
SA198256G5_PS-R-01_15-06-20162
SA198257G4_PS-WHI-012_01-05-20182
SA198258D2D16_A-18_2017-04-042
SA198259G3_PS-A-14_26-06-20172
SA198260G3_PS-C-17_20-06-20172
SA198261D2D16_WHI-026_2018-07-192
SA198262D2D16_WHI-023_2018-08-292
SA198263D2D16_A-56_2017-08-232
SA198264G5_PS-MC-13_27-02-20182
SA198265D2D16_WHI-014_2018-06-052
SA198266G4_PS-A-62_15-02-20182
SA198267G3_PS-R-12_02-03-20172
SA198268G4_PS-R-17_14-06-20172
SA198269G5_PS-MC-07_14-08-20172
SA198270G4_PS-R-05_17-08-20162
SA198271G4_PS-MC-10_06-03-20182
SA198272D17_R-20_2017-11-223
SA198273D17_A-64_2018-07-063
SA198274R-11_2016-11-043
SA198275D17_R-23_2017-12-133
SA198276D17_WHI-004_2018-03-163
SA198277MC-01_2017-07-053
SA198278MC-08_2017-09-143
SA198279D2D16_WHI-010_2018-04-233
SA198280G5_PS-WHI-103_30-04-20183
SA198281G5_PS-R-28_20-06-20183
SA198282G6_PS-R-25_11-01-20183
SA198283G5_PS-A-67_25-06-20183
SA198284MC-04_2017-07-254
SA198285R-31_2018-07-254+
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Collection:

Collection ID:CO002159
Collection Summary:The study was conducted at six sites (hospitals and hospices) in the North West of England (UK) from June 2016 to September 2018. Patients with lung cancer were recruited prospectively and multiple urine samples were collected up to three times a week while an inpatient. Ethical approval was provided by North Wales (West) Research Ethics Committee (REC reference 15/WA/0464). Research team members collected 20 mL of urine from participants in a universal container. For those participants with a urinary catheter, the urine was collected using a needle and syringe from the catheter port. The samples were stored on site in a locked freezer at −20°C. An anonymised record of the medication administered was collected. Collection protocol described previously by Coyle et al. (BMJ Open. 2016; 6(11) e011763. doi: 10.1136/bmjopen-2016-011763).
Sample Type:Urine
Volumeoramount Collected:20 mL
Storage Conditions:-20℃

Treatment:

Treatment ID:TR002178
Treatment Summary:Clinical observational study. Serial urine samples collected from patients with lung cancer in a palliative care setting at varying time points up until death; >12 weeks - 1 week before death (see 'study design' information). Although multiple samples were collected from patients, only the final sample was included in the analysis.

Sample Preparation:

Sampleprep ID:SP002172
Sampleprep Summary:Individual patient samples were thawed at room temperature, vortexed and separated into four replicate aliquots in individual 96-well plates (Waters, UK) which were stored at -80 °C until analysis by one of four different methods; two different chromatography conditions in negative and positive ionisation polarity. Pooled quality control samples were created following the protocol described by Norman et al. (Clin Chem. 2019;65(4):530-39. doi: 10.1373/clinchem.2018.295345). For each sample group (time before death), a separate representative pool was created by pooling an equal volume of each individual urine sample for quality control purposes. An overall pool was also created by pooling equal proportions of the above group pools. Analysis of individual and pooled samples was performed following dilution of 1:3 urine:deionised water (DIRECT-Q 3UV Millipore water purification system) as previously described by Norman et al. (2019).

Combined analysis:

Analysis ID AN003396 AN003397 AN003398 AN003399
Analysis type MS MS MS MS
Chromatography type Reversed phase Reversed phase Normal phase Normal phase
Chromatography system Agilent 6550 Agilent 6550 Agilent 6550 Agilent 6550
Column Waters Atlantis dC18 (3x100 mm, 3 µm) Waters Atlantis dC18 (3x100 mm, 3 µm) Waters BEH Amide (3x150 mm, 1.7 µm) Waters BEH Amide (3x150 mm, 1.7 µm)
MS Type ESI ESI ESI ESI
MS instrument type QTOF QTOF QTOF QTOF
MS instrument name Agilent 6550 QTOF Agilent 6550 QTOF Agilent 6550 QTOF Agilent 6550 QTOF
Ion Mode NEGATIVE POSITIVE NEGATIVE POSITIVE
Units Values are raw peak area raw peak area raw peak area Values are raw peak area

Chromatography:

Chromatography ID:CH002511
Chromatography Summary:LC method 1: employed an Atlantis dC18 column (3x100 mm, 3 µm, Waters, UK) maintained at 60 °C with flow rate at 0.4 mL/min. Mobile phases were (A) water and (B) methanol both containing 5 mmol/L ammonium formate and 0.1 % formic acid. The elution gradient started at 5 % B at 0 to 1 min increasing linearly to 100 % by 12 min, held at 100 % B until 14 min, returning to 95 % A for 5 min.
Instrument Name:Agilent 6550
Column Name:Waters Atlantis dC18 (3x100 mm, 3 µm)
Chromatography Type:Reversed phase
  
Chromatography ID:CH002512
Chromatography Summary:LC method 2: used a BEH amide column (3x150 mm, 1.7 µm, Waters, UK) maintained at 40 °C with flow rate at 0.6 mL/min. Mobile phases were (A) water and (B) acetonitrile both containing 0.1 % formic acid. The elution gradient started at 99 % B, decreasing linearly to 30 % from 1 to 12 min, held at 30 % B until 12.6 min, returning to 99 % B for 3.4 min. Sample injection volume was 1 µL for both LC methods.
Instrument Name:Agilent 6550
Column Name:Waters BEH Amide (3x150 mm, 1.7 µm)
Chromatography Type:Normal phase

MS:

MS ID:MS003163
Analysis ID:AN003396
Instrument Name:Agilent 6550 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:Mass spectrometry conditions: The mass spectrometer was tuned and calibrated according to protocols recommended by the manufacturer. Acquisition was performed in 2 GHz mode and mass range 50-1700. The capillary voltage was 4000 V and fragmentor voltage 380 V. The desolvation gas temperature was 200 °C with flow rate at 15 L/min. The sheath gas temperature was 300 °C with flow rate at 12 L/min. The nebulizer pressure was 40 psig and nozzle voltage 1000 V (± for positive and negative ionisation modes). The acquisition rate was 3 spectra/second. The reference mass solution was continually infused at a flow rate of 0.5 mL/min by a separate isocratic pump for constant mass correction. Repeat injections of each pooled sample were interspersed throughout the analytical sequence, as per the quality control procedure described by Norman et al. (Clin Chem. 2019;65(4):530-539. doi: 10.1373/clinchem.2018.295345). The individual sample analysis order was randomised computationally. Data Pre-processing and Quality Control: All data were acquired using the MassHunter suite (Agilent build 6.0) with quality checks being performed by Qualitative Analysis (build 07.00). Mass accuracy was checked using extracted ion chromatograms of reference masses: the resulting accuracy was ±5 ppm during the run. Additionally, chromatographic reproducibility was checked by overlaying binary pump pressure curves across each analytical sequence. Data was filtered based upon the pooled QC samples, with compounds being retained if observed in 100 % of replicate injections for at least 1 pool and with peak area coefficient of variation (CV) <25 % across all replicate injections for each pool. A comprehensive semi-targeted approach was employed to assign the identity of urinary metabolites using an in-house compound library that included a broad range of metabolites involved in intermediary metabolism. Targeted feature extraction was performed on each dataset based on matching of metabolite chemical features against an accurate mass and retention time (AMRT) database previously generated from analysis of the IROA Technology MS metabolite library of tandards by each LC method described above, combined with the same QTOF analytical parameters used in this study (databases publicly available: https://doi.org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time, MS/MS (data-dependent, employed for 'hit' metabolites) was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al. Feature extraction was performed in MassHunter Profinder (build 10.0); accurate mass window of 10 ppm and retention time window of 0.3 min against the respective AMRT database. Data were exported in the format of a csv file for statistical analysis. Unidentified compounds are 'known unknowns' of interest from previous experiments; detection by matched AMRT and named in the format 'RT_neutral-mass'. 'BDRM'; unidentified bone-derived metabolite.
Ion Mode:NEGATIVE
  
MS ID:MS003164
Analysis ID:AN003397
Instrument Name:Agilent 6550 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:Mass spectrometry conditions: The mass spectrometer was tuned and calibrated according to protocols recommended by the manufacturer. Acquisition was performed in 2 GHz mode and mass range 50-1700. The capillary voltage was 4000 V and fragmentor voltage 380 V. The desolvation gas temperature was 200 °C with flow rate at 15 L/min. The sheath gas temperature was 300 °C with flow rate at 12 L/min. The nebulizer pressure was 40 psig and nozzle voltage 1000 V (± for positive and negative ionisation modes). The acquisition rate was 3 spectra/second. The reference mass solution was continually infused at a flow rate of 0.5 mL/min by a separate isocratic pump for constant mass correction. Repeat injections of each pooled sample were interspersed throughout the analytical sequence, as per the quality control procedure described by Norman et al. (Clin Chem. 2019;65(4):530-539. doi: 10.1373/clinchem.2018.295345). The individual sample analysis order was randomised computationally. Data Pre-processing and Quality Control: All data were acquired using the MassHunter suite (Agilent build 6.0) with quality checks being performed by Qualitative Analysis (build 07.00). Mass accuracy was checked using extracted ion chromatograms of reference masses: the resulting accuracy was ±5 ppm during the run. Additionally, chromatographic reproducibility was checked by overlaying binary pump pressure curves across each analytical sequence. Data was filtered based upon the pooled QC samples, with compounds being retained if observed in 100 % of replicate injections for at least 1 pool and with peak area coefficient of variation (CV) <25 % across all replicate injections for each pool. A comprehensive semi-targeted approach was employed to assign the identity of urinary metabolites using an in-house compound library that included a broad range of metabolites involved in intermediary metabolism. Targeted feature extraction was performed on each dataset based on matching of metabolite chemical features against an accurate mass and retention time (AMRT) database previously generated from analysis of the IROA Technology MS metabolite library of tandards by each LC method described above, combined with the same QTOF analytical parameters used in this study (databases publicly available: https://doi.org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time, MS/MS (data-dependent, employed for 'hit' metabolites) was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al. Feature extraction was performed in MassHunter Profinder (build 10.0); accurate mass window of 10 ppm and retention time window of 0.3 min against the respective AMRT database. Data were exported in the format of a csv file for statistical analysis. Unidentified compounds are 'known unknowns' of interest from previous experiments; detection by matched AMRT and named in the format 'RT_neutral-mass'. 'BDRM'; unidentified bone-derived metabolite.
Ion Mode:POSITIVE
  
MS ID:MS003165
Analysis ID:AN003398
Instrument Name:Agilent 6550 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:Mass spectrometry conditions: The mass spectrometer was tuned and calibrated according to protocols recommended by the manufacturer. Acquisition was performed in 2 GHz mode and mass range 50-1700. The capillary voltage was 4000 V and fragmentor voltage 380 V. The desolvation gas temperature was 200 °C with flow rate at 15 L/min. The sheath gas temperature was 300 °C with flow rate at 12 L/min. The nebulizer pressure was 40 psig and nozzle voltage 1000 V (± for positive and negative ionisation modes). The acquisition rate was 3 spectra/second. The reference mass solution was continually infused at a flow rate of 0.5 mL/min by a separate isocratic pump for constant mass correction. Repeat injections of each pooled sample were interspersed throughout the analytical sequence, as per the quality control procedure described by Norman et al. (Clin Chem. 2019;65(4):530-539. doi: 10.1373/clinchem.2018.295345). The individual sample analysis order was randomised computationally. Data Pre-processing and Quality Control: All data were acquired using the MassHunter suite (Agilent build 6.0) with quality checks being performed by Qualitative Analysis (build 07.00). Mass accuracy was checked using extracted ion chromatograms of reference masses: the resulting accuracy was ±5 ppm during the run. Additionally, chromatographic reproducibility was checked by overlaying binary pump pressure curves across each analytical sequence. Data was filtered based upon the pooled QC samples, with compounds being retained if observed in 100 % of replicate injections for at least 1 pool and with peak area coefficient of variation (CV) <25 % across all replicate injections for each pool. A comprehensive semi-targeted approach was employed to assign the identity of urinary metabolites using an in-house compound library that included a broad range of metabolites involved in intermediary metabolism. Targeted feature extraction was performed on each dataset based on matching of metabolite chemical features against an accurate mass and retention time (AMRT) database previously generated from analysis of the IROA Technology MS metabolite library of tandards by each LC method described above, combined with the same QTOF analytical parameters used in this study (databases publicly available: https://doi.org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time, MS/MS (data-dependent, employed for 'hit' metabolites) was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al. Feature extraction was performed in MassHunter Profinder (build 10.0); accurate mass window of 10 ppm and retention time window of 0.3 min against the respective AMRT database. Data were exported in the format of a csv file for statistical analysis.
Ion Mode:NEGATIVE
  
MS ID:MS003166
Analysis ID:AN003399
Instrument Name:Agilent 6550 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:Mass spectrometry conditions: The mass spectrometer was tuned and calibrated according to protocols recommended by the manufacturer. Acquisition was performed in 2 GHz mode and mass range 50-1700. The capillary voltage was 4000 V and fragmentor voltage 380 V. The desolvation gas temperature was 200 °C with flow rate at 15 L/min. The sheath gas temperature was 300 °C with flow rate at 12 L/min. The nebulizer pressure was 40 psig and nozzle voltage 1000 V (± for positive and negative ionisation modes). The acquisition rate was 3 spectra/second. The reference mass solution was continually infused at a flow rate of 0.5 mL/min by a separate isocratic pump for constant mass correction. Repeat injections of each pooled sample were interspersed throughout the analytical sequence, as per the quality control procedure described by Norman et al. (Clin Chem. 2019;65(4):530-539. doi: 10.1373/clinchem.2018.295345). The individual sample analysis order was randomised computationally. Data Pre-processing and Quality Control: All data were acquired using the MassHunter suite (Agilent build 6.0) with quality checks being performed by Qualitative Analysis (build 07.00). Mass accuracy was checked using extracted ion chromatograms of reference masses: the resulting accuracy was ±5 ppm during the run. Additionally, chromatographic reproducibility was checked by overlaying binary pump pressure curves across each analytical sequence. Data was filtered based upon the pooled QC samples, with compounds being retained if observed in 100 % of replicate injections for at least 1 pool and with peak area coefficient of variation (CV) <25 % across all replicate injections for each pool. A comprehensive semi-targeted approach was employed to assign the identity of urinary metabolites using an in-house compound library that included a broad range of metabolites involved in intermediary metabolism. Targeted feature extraction was performed on each dataset based on matching of metabolite chemical features against an accurate mass and retention time (AMRT) database previously generated from analysis of the IROA Technology MS metabolite library of tandards by each LC method described above, combined with the same QTOF analytical parameters used in this study (databases publicly available: https://doi.org/10.6084/m9.figshare.c.4378235.v2). In addition to accurate mass and retention time, MS/MS (data-dependent, employed for 'hit' metabolites) was also used in the confirmation of metabolite identity (i.e. level 1 identification) as per Sumner et al. Feature extraction was performed in MassHunter Profinder (build 10.0); accurate mass window of 10 ppm and retention time window of 0.3 min against the respective AMRT database. Data were exported in the format of a csv file for statistical analysis.
Ion Mode:POSITIVE
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