Comparison Log
2024-12-01 06:28:51.815006
mwtab Python Library Version: 1.2.5
Source:      https://www.metabolomicsworkbench.org/rest/study/analysis_id/AN004961/mwtab/...
Study ID:    ST003026
Analysis ID: AN004961
Status:      Inconsistent

Sections "MS" contain missmatched items: {('MS_COMMENTS', "For each injection, the parameters of the ESI source were configured as follows: The capillary voltage was adjusted to 4500 V, the flow rate of the drying gas was set at 10.0 l/min with a temperature of 220 °C, and the nebulizer pressure was held steady at 2.2 bar. In the MS2 acquisition phase, the collision energy stepping spanned from 100 to 250%, maintaining a constant value of 20 eV, and an end plate offset of 500 V. To perform the external calibration process, sodium formate served as the calibrant. The acquisition process was divided into two segments: the auto MS scan segment, spanning from 0 to 0.3 minutes, and the auto MS/MS segment, encompassing fragmentation, lasting from 0.3 to 30 minutes. Both segments were executed in the positive mode at a frequency of 12 Hz. The automatic in-run mass scan range covered from 20 to 1300 m/z, with a precursor ion width of ±0.5. Three precursors were chosen per cycle with a cycle time of 0.5 seconds, and the threshold was established at 400 counts. Active exclusion was initiated after three spectra and lifted after 0.2 minutes.The acquired data underwent analysis through MetaboScape® 4.0 software (Bruker Daltonics, Billerica, MA, USA). For the processed data, the T-ReX 2D/3D workflow employed bucketing parameters that included an intensity threshold of 1000, a peak length spanning 7 spectra, and the utilization of peak area for quantification. Mass spectra calibration was executed within the 0-0.3-minute range, utilizing features from a minimum of 50 to 148 samples. The auto MS/MS scan followed the average method, with a retention time range from 0.3 to 25 minutes and a mass range of 50 to 1000 m/z. The LC-QTOF analysis involved duplicate samples obtained from a collective of 74 participants across all groups. After merging these samples, a dataset comprising 3763 unique features was generated. The identification of metabolites was accomplished by aligning the MS/MS spectra and retention time with the HMBD 4.0 database, meticulously crafted to address the specific needs of the metabolomics community. Following filtration using MetaboScape®, a comprehensive set of 85 distinct metabolites was chosen. The peak intensities of each metabolite were employed to construct the quantitative data matrix. Only metabolites demonstrating statistical significance, with a p-value of less than 0.05 and documented in the human metabolome database 4.0 (HMDB), were incorporated into the metabolite datasets. The online website HMDB (https://hmdb.ca/metabolites/HMDB0059911) was used to filter the human metabolites. Following HMDB filtration, 82 unique metabolites remained. The metabolite datasets were exported as CSV files and subsequently imported into the MetaboAnalyst 5.0 software—a comprehensive metabolomics data analysis platform created by McGill University in Montreal, QC, Canada. For sample classification, the sparse partial least squares-discriminant analysis (sPLS-DA) method in MetaboAnalyst was employed to select the most distinguishing features within the studied group. This process aimed to minimize the rate of false positives, and corrections for multiple hypothesis testing were applied using the false discovery rate (FDR) approach. The identification of significantly altered metabolites in the overweight or obese group, as opposed to the normal weight group, was accomplished through a two-tailed independent Student''s t-test. This led to the creation of a volcano plot, visually representing the statistical significance and fold change (p<0.05, FC=1.25), highlighting the dysregulation of cellular metabolites for each condition. Furthermore, a one-way analysis of variance (ANOVA) was applied for a comprehensive comparison across multiple groups, encompassing normal weight, overweight, and obese groups. The threshold for significance was p<0.05. Functional Enrichments were constructed using Metaboanalyst (https://www.metaboanalyst.ca). Additionally, MetaboAnalyst 5.0 was utilized for the enrichment metabolite sets, and pathway analysis. Venn diagram was generated using (http://bioinformatics.psb.ugent.be/webtools/Venn/)."), ('MS_COMMENTS', "For each injection, the parameters of the ESI source were configured as follows: The capillary voltage was adjusted to 4500 V, the flow rate of the drying gas was set at 10.0 l/min with a temperature of 220 °C, and the nebulizer pressure was held steady at 2.2 bar. In the MS2 acquisition phase, the collision energy stepping spanned from 100 to 250%, maintaining a constant value of 20 eV, and an end plate offset of 500 V. To perform the external calibration process, sodium formate served as the calibrant. The acquisition process was divided into two segments: the auto MS scan segment, spanning from 0 to 0.3 minutes, and the auto MS/MS segment, encompassing fragmentation, lasting from 0.3 to 30 minutes. Both segments were executed in the positive mode at a frequency of 12 Hz. The automatic in-run mass scan range covered from 20 to 1300 m/z, with a precursor ion width of ±0.5. Three precursors were chosen per cycle with a cycle time of 0.5 seconds, and the threshold was established at 400 counts. Active exclusion was initiated after three spectra and lifted after 0.2 minutes.The acquired data underwent analysis through MetaboScape® 4.0 software (Bruker Daltonics, Billerica, MA, USA). For the processed data, the T-ReX 2D/3D workflow employed bucketing parameters that included an intensity threshold of 1000, a peak length spanning 7 spectra, and the utilization of peak area for quantification. Mass spectra calibration was executed within the 0-0.3-minute range, utilizing features from a minimum of 50 to 148 samples. The auto MS/MS scan followed the average method, with a retention time range from 0.3 to 25 minutes and a mass range of 50 to 1000 m/z. The LC-QTOF analysis involved duplicate samples obtained from a collective of 74 participants across all groups. After merging these samples, a dataset comprising 3763 unique features was generated. The identification of metabolites was accomplished by aligning the MS/MS spectra and retention time with the HMBD 4.0 database, meticulously crafted to address the specific needs of the metabolomics community. Following filtration using MetaboScape®, a comprehensive set of 85 distinct metabolites was chosen. The peak intensities of each metabolite were employed to construct the quantitative data matrix. Only metabolites demonstrating statistical significance, with a p-value of less than 0.05 and documented in the human metabolome database 4.0 (HMDB), were incorporated into the metabolite datasets. The online website HMDB (https://hmdb.ca/metabolites/HMDB0059911) was used to filter the human metabolites. Following HMDB filtration, 82 unique metabolites remained. The metabolite datasets were exported as CSV files and subsequently imported into the MetaboAnalyst 5.0 software—a comprehensive metabolomics data analysis platform created by McGill University in Montreal, QC, Canada. For sample classification, the sparse partial least squares-discriminant analysis (sPLS-DA) method in MetaboAnalyst was employed to select the most distinguishing features within the studied group. This process aimed to minimize the rate of false positives, and corrections for multiple hypothesis testing were applied using the false discovery rate (FDR) approach. The identification of significantly altered metabolites in the overweight or obese group, as opposed to the normal weight group, was accomplished through a two-tailed independent Student's t-test. This led to the creation of a volcano plot, visually representing the statistical significance and fold change (p<0.05, FC=1.25), highlighting the dysregulation of cellular metabolites for each condition. Furthermore, a one-way analysis of variance (ANOVA) was applied for a comprehensive comparison across multiple groups, encompassing normal weight, overweight, and obese groups. The threshold for significance was p<0.05. Functional Enrichments were constructed using Metaboanalyst (https://www.metaboanalyst.ca). Additionally, MetaboAnalyst 5.0 was utilized for the enrichment metabolite sets, and pathway analysis. Venn diagram was generated using (http://bioinformatics.psb.ugent.be/webtools/Venn/).")}
Sections "SAMPLEPREP" contain missmatched items: {('SAMPLEPREP_SUMMARY', "Upon aliquoting the samples into 100 µl Eppendorf tubes, 300 µl of methanol (sourced from Wunstorfer Strasse, Seelze, Germany) was introduced. The tubes underwent thorough mixing with a vortex mixer and were subsequently incubated at –20 °C for 2 hours. After this period, the samples were vortexed again and centrifuged for 15 minutes at 14,000 rpm. The resulting supernatant underwent evaporation at 35–40 °C. To guarantee the analysis''s consistency and reliability, a quality control (QC) sample was prepared by combining an equal volume (10 µl) from each individual sample. This QC sample was injected into the system after every 9-10 samples to evaluate the analysis''s reproducibility. Before injection, the extracted samples were reconstituted in 250 µl of 0.1% formic acid in deionized water, using Honeywell''s LC-MS CHROMASOLV, situated in Wunstorfer Strasse, Seelze, Germany. Following the completion of sample preparation, the supernatant underwent filtration for subsequent LC-MS/MS analysis. This filtration utilized a hydrophilic nylon syringe filter with a pore size of 0.45 µm. The filtered sample was meticulously collected within a specialized insert positioned inside LC glass vials, ensuring its integrity for further analysis."), ('SAMPLEPREP_SUMMARY', "Upon aliquoting the samples into 100 µl Eppendorf tubes, 300 µl of methanol (sourced from Wunstorfer Strasse, Seelze, Germany) was introduced. The tubes underwent thorough mixing with a vortex mixer and were subsequently incubated at –20 °C for 2 hours. After this period, the samples were vortexed again and centrifuged for 15 minutes at 14,000 rpm. The resulting supernatant underwent evaporation at 35–40 °C. To guarantee the analysis's consistency and reliability, a quality control (QC) sample was prepared by combining an equal volume (10 µl) from each individual sample. This QC sample was injected into the system after every 9-10 samples to evaluate the analysis's reproducibility. Before injection, the extracted samples were reconstituted in 250 µl of 0.1% formic acid in deionized water, using Honeywell's LC-MS CHROMASOLV, situated in Wunstorfer Strasse, Seelze, Germany. Following the completion of sample preparation, the supernatant underwent filtration for subsequent LC-MS/MS analysis. This filtration utilized a hydrophilic nylon syringe filter with a pore size of 0.45 µm. The filtered sample was meticulously collected within a specialized insert positioned inside LC glass vials, ensuring its integrity for further analysis.")}
Sections "TREATMENT" contain missmatched items: {('TREATMENT_SUMMARY', "No treatment. The participants'' ages ranged from 18 to 75 years. We categorized our study population into three groups based on participants'' BMI values, glycemic parameters, and the presence of at least two components of Metabolic Syndrome (MetS), along with central obesity, as per the definition specified by the International Diabetes Federation (IDF). Recruiters were divided into three groups: 1. Group 1 (Normal weight individuals as control): Normoglycemic (with HbA1c<5.7% or FPG <100 mg/dL) and lean with 19.5< BMI kg\\m² < 25. 2. Group 2 (Overweight individuals): Non-diabetic subjects as well as overweight of BMI ≥25 kg/m2 having three or more of the MetS components as delineated by the International Diabetes Federation (IDF). 3. Group 3 (Obese individuals): Non-diabetic subjects as well as obese of BMI ≥ 30 kg/m2 having three or more of the MetS components as delineated by the International Diabetes Federation (IDF)."), ('TREATMENT_SUMMARY', "No treatment. The participants' ages ranged from 18 to 75 years. We categorized our study population into three groups based on participants' BMI values, glycemic parameters, and the presence of at least two components of Metabolic Syndrome (MetS), along with central obesity, as per the definition specified by the International Diabetes Federation (IDF). Recruiters were divided into three groups: 1. Group 1 (Normal weight individuals as control): Normoglycemic (with HbA1c<5.7% or FPG <100 mg/dL) and lean with 19.5< BMI kgm² < 25. 2. Group 2 (Overweight individuals): Non-diabetic subjects as well as overweight of BMI ≥25 kg/m2 having three or more of the MetS components as delineated by the International Diabetes Federation (IDF). 3. Group 3 (Obese individuals): Non-diabetic subjects as well as obese of BMI ≥ 30 kg/m2 having three or more of the MetS components as delineated by the International Diabetes Federation (IDF).")}