Comparison Log 2025-12-15 02:42:37.203124 mwtab Python Library Version: 2.0.0 Source: https://www.metabolomicsworkbench.org/rest/study/analysis_id/AN006028/mwtab/... Study ID: ST003671 Analysis ID: AN006028 Status: Inconsistent Sections "PROJECT" contain missmatched items: {'PROJECT_SUMMARY': ["Gestational diabetes mellitus (GDM) is a predominant medical complication Aims: Gestational diabetes mellitus (GDM) is a predominant medical complication during pregnancy, which is characterized by a glucose intolerance identified during pregnancy and generally resolved after childbirth. Women with a GDM history are 7 times more subject to develop type 2 diabetes compared to healthy pregnant women. Metabolic alteration during pregnancy has been suggested as an underlying cause of GDM. Metabolomics of collected blood from GDM pregnant women indicate that GDM is associated with alteration of amino acids, fatty acids, and glycolysis. However, deep metabolomics analysis to identify complicated and unique pathways that can guide prognosis and treatment are not fully determined particularly with Middle Eastern women. Developing such data in comparison to published data will help to provide more understanding of the disease progression and for target treatment. Methods: In this research, we utilized metabolomics-assisted technology to identify dysregulated metabolic pathways and metabolites in pregnant women with GDM compared to healthy pregnant women. Trapped ion mobility spectrometry time-of-flight TIMS-QTOF MS was employed on blood samples collected from 32 GDM pregnant women in comparison to 20 healthy pregnant women. This was followed by significant statistical analysis including two-tailed independent Student's t-test, P<0.05 will be considered significant. These findings facilitate clear understanding of underlying metabolic pathways and early detection of GDM and hence will enable effective targeting treatment. Results: The student t-test analysis was used, 33 of the 108 metabolites that were discovered had statistical significance (p<0.05) when compared between the two groups. When compared to the control group, which consists of normal pregnant women, the gestational diabetes group had significantly lower levels of citramalic acid, creatinine, D-arginine, and glutamine, while the group with gestational diabetes had higher levels of 4-aminohippuric acid, homovanillic acid, alpha-aspartyl-lysine, L-aspartyl-L-phenvlalanine, L-valine, L-leucine, and normetanepherine. Conclusion: Understanding specific metabolic interactions aids pregnant GDM patients in comprehending many factors affecting their metabolic environment. In addition to enhancing our understanding of the molecular mechanisms underlying GDM, this knowledge makes it possible to explore targeted therapeutics for its management or prevention by altering unusual metabolomic pathways.", "Gestational diabetes mellitus (GDM) is a predominant medical complication Aims: Gestational diabetes mellitus (GDM) is a predominant medical complication during pregnancy, which is characterized by a glucose intolerance identified during pregnancy and generally resolved after childbirth. Women with a GDM history are 7 times more subject to develop type 2 diabetes compared to healthy pregnant women. Metabolic alteration during pregnancy has been suggested as an underlying cause of GDM. Metabolomics of collected blood from GDM pregnant women indicate that GDM is associated with alteration of amino acids, fatty acids, and glycolysis. However, deep metabolomics analysis to identify complicated and unique pathways that can guide prognosis and treatment are not fully determined particularly with Middle Eastern women. Developing such data in comparison to published data will help to provide more understanding of the disease progression and for target treatment. Methods: In this research, we utilized metabolomics-assisted technology to identify dysregulated metabolic pathways and metabolites in pregnant women with GDM compared to healthy pregnant women. Trapped ion mobility spectrometry time-of-flight TIMS-QTOF MS was employed on blood samples collected from 32 GDM pregnant women in comparison to 20 healthy pregnant women. This was followed by significant statistical analysis including two-tailed independent Student''s t-test, P<0.05 will be considered significant. These findings facilitate clear understanding of underlying metabolic pathways and early detection of GDM and hence will enable effective targeting treatment. Results: The student t-test analysis was used, 33 of the 108 metabolites that were discovered had statistical significance (p<0.05) when compared between the two groups. When compared to the control group, which consists of normal pregnant women, the gestational diabetes group had significantly lower levels of citramalic acid, creatinine, D-arginine, and glutamine, while the group with gestational diabetes had higher levels of 4-aminohippuric acid, homovanillic acid, alpha-aspartyl-lysine, L-aspartyl-L-phenvlalanine, L-valine, L-leucine, and normetanepherine. Conclusion: Understanding specific metabolic interactions aids pregnant GDM patients in comprehending many factors affecting their metabolic environment. In addition to enhancing our understanding of the molecular mechanisms underlying GDM, this knowledge makes it possible to explore targeted therapeutics for its management or prevention by altering unusual metabolomic pathways."]} Sections "SAMPLEPREP" contain missmatched items: {'SAMPLEPREP_SUMMARY': ["After the samples were aliquoted into 100 µL Eppendorf tubes, 300 µL of methanol (from Wunstorfer Strasse, Seelze, Germany) was added. After the tubes were well combined using a vortex mixer, they were incubated for two hours at -20 °C. Following this time, the samples underwent another vortex and were centrifuged at 14,000 rpm for 15 minutes. At 35–40 °C, the resultant supernatant evaporated. To ensure the consistency and reliability of the analysis, a quality control (QC) sample was created by combining an equal volume (10 µL) from each sample. To assess the reproducibility of the analysis, this QC sample was added to the system every nine to ten samples. Using Honeywell's LC-MS CHROMASOLV in Wunstorfer Strasse, Seelze, Germany, the extracted samples were reconstituted in 250 µL of 0.1% formic acid in deionized water prior to injection. The supernatant was filtered after sample preparation in order to prepare it for LC-MS/MS analysis. A 0.45 µm pore size hydrophilic nylon syringe filter was used for this filtration. To preserve the integrity of the filtered sample for subsequent analysis, it was carefully collected and placed inside LC glass vials using a specialized insert.", "After the samples were aliquoted into 100 µL Eppendorf tubes, 300 µL of methanol (from Wunstorfer Strasse, Seelze, Germany) was added. After the tubes were well combined using a vortex mixer, they were incubated for two hours at -20 °C. Following this time, the samples underwent another vortex and were centrifuged at 14,000 rpm for 15 minutes. At 35–40 °C, the resultant supernatant evaporated. To ensure the consistency and reliability of the analysis, a quality control (QC) sample was created by combining an equal volume (10 µL) from each sample. To assess the reproducibility of the analysis, this QC sample was added to the system every nine to ten samples. Using Honeywell''s LC-MS CHROMASOLV in Wunstorfer Strasse, Seelze, Germany, the extracted samples were reconstituted in 250 µL of 0.1% formic acid in deionized water prior to injection. The supernatant was filtered after sample preparation in order to prepare it for LC-MS/MS analysis. A 0.45 µm pore size hydrophilic nylon syringe filter was used for this filtration. To preserve the integrity of the filtered sample for subsequent analysis, it was carefully collected and placed inside LC glass vials using a specialized insert."]} Sections "STUDY" contain missmatched items: {'STUDY_SUMMARY': ["Aims: Gestational diabetes mellitus (GDM) is a predominant medical complication during pregnancy, which is characterized by a glucose intolerance identified during pregnancy and generally resolved after childbirth. Women with a GDM history are 7 times more subject to develop type 2 diabetes compared to healthy pregnant women. Metabolic alteration during pregnancy has been suggested as an underlying cause of GDM. Metabolomics of collected blood from GDM pregnant women indicate that GDM is associated with alteration of amino acids, fatty acids, and glycolysis. However, deep metabolomics analysis to identify complicated and unique pathways that can guide prognosis and treatment are not fully determined particularly with Middle Eastern women. Developing such data in comparison to published data will help to provide more understanding of the disease progression and for target treatment. Methods: In this research, we utilized metabolomics-assisted technology to identify dysregulated metabolic pathways and metabolites in pregnant women with GDM compared to healthy pregnant women. Trapped ion mobility spectrometry time-of-flight TIMS-QTOF MS was employed on blood samples collected from 32 GDM pregnant women in comparison to 20 healthy pregnant women. This was followed by significant statistical analysis including two-tailed independent Student's t-test, P<0.05 will be considered significant. These findings facilitate clear understanding of underlying metabolic pathways and early detection of GDM and hence will enable effective targeting treatment. Results: The student t-test analysis was used, 33 of the 108 metabolites that were discovered had statistical significance (p<0.05) when compared between the two groups. When compared to the control group, which consists of normal pregnant women, the gestational diabetes group had significantly lower levels of citramalic acid, creatinine, D-arginine, and glutamine, while the group with gestational diabetes had higher levels of 4-aminohippuric acid, homovanillic acid, alpha-aspartyl-lysine, L-aspartyl-L-phenvlalanine, L-valine, L-leucine, and normetanepherine. Conclusion: Understanding specific metabolic interactions aids pregnant GDM patients in comprehending many factors affecting their metabolic environment. In addition to enhancing our understanding of the molecular mechanisms underlying GDM, this knowledge makes it possible to explore targeted therapeutics for its management or prevention by altering unusual metabolomic pathways.", "Aims: Gestational diabetes mellitus (GDM) is a predominant medical complication during pregnancy, which is characterized by a glucose intolerance identified during pregnancy and generally resolved after childbirth. Women with a GDM history are 7 times more subject to develop type 2 diabetes compared to healthy pregnant women. Metabolic alteration during pregnancy has been suggested as an underlying cause of GDM. Metabolomics of collected blood from GDM pregnant women indicate that GDM is associated with alteration of amino acids, fatty acids, and glycolysis. However, deep metabolomics analysis to identify complicated and unique pathways that can guide prognosis and treatment are not fully determined particularly with Middle Eastern women. Developing such data in comparison to published data will help to provide more understanding of the disease progression and for target treatment. Methods: In this research, we utilized metabolomics-assisted technology to identify dysregulated metabolic pathways and metabolites in pregnant women with GDM compared to healthy pregnant women. Trapped ion mobility spectrometry time-of-flight TIMS-QTOF MS was employed on blood samples collected from 32 GDM pregnant women in comparison to 20 healthy pregnant women. This was followed by significant statistical analysis including two-tailed independent Student''s t-test, P<0.05 will be considered significant. These findings facilitate clear understanding of underlying metabolic pathways and early detection of GDM and hence will enable effective targeting treatment. Results: The student t-test analysis was used, 33 of the 108 metabolites that were discovered had statistical significance (p<0.05) when compared between the two groups. When compared to the control group, which consists of normal pregnant women, the gestational diabetes group had significantly lower levels of citramalic acid, creatinine, D-arginine, and glutamine, while the group with gestational diabetes had higher levels of 4-aminohippuric acid, homovanillic acid, alpha-aspartyl-lysine, L-aspartyl-L-phenvlalanine, L-valine, L-leucine, and normetanepherine. Conclusion: Understanding specific metabolic interactions aids pregnant GDM patients in comprehending many factors affecting their metabolic environment. In addition to enhancing our understanding of the molecular mechanisms underlying GDM, this knowledge makes it possible to explore targeted therapeutics for its management or prevention by altering unusual metabolomic pathways."]} Sections "MS" contain missmatched items: {'MS_COMMENTS': ["Sodium formate was the calibrant used in the external calibration procedure. The two parts of the acquisition procedure were the auto MS scan, which took 0 to 0.3 minutes, and the auto MS/MS, which took 0.3 to 30 minutes and included fragmentation. At 12 Hz, both segments were run in the positive mode. The automatic in-run mass scan range was ±0.5 for the precursor ion width and covered 20–1300 m/z. For every cycle of 0.5 seconds, three precursors were selected, and 400 counts served as the threshold. Following three spectra, active exclusion was started, and it was lifted after 0.2 minutes.The MetaboScape® 4.0 software (Bruker Daltonics, Billerica, MA, USA) was used to analyze the collected data. The T-ReX 2D/3D workflow used bucketing parameters for the processed data, which included a peak length spanning 7 spectra, an intensity threshold of 1000, and peak area for quantification. Mass spectra calibration was performed in the range of 0-0.3 min, using features from at least 50 to 186 samples. With a mass range of 50 to 1300 m/z and a retention time range of 0.3 to 25 minutes, the auto MS/MS scan adhered to the average methodology. For the LC-QTOF analysis, duplicate samples from 52 individuals in each group were collected. Following the combination of these samples, a dataset with 3625 distinct features was produced. By matching the retention duration and MS/MS spectra to the HMBD 4.0 database—carefully designed to meet the unique requirements of the metabolomics community—metabolites were identified. After filtering with MetaboScape®, an extensive collection of 108 unique metabolites was selected. The quantitative data matrix was created using each metabolite's peak intensities. The metabolite datasets contained only those metabolites that were statistically significant (p-value < 0.05*) and listed in the human metabolome database 4.0 (HMDB). The human metabolites were filtered using the HMDB online database (https://hmdb.ca/metabolites/HMDB0059911). There were still 108 distinct metabolites after HMDB filtration. The MetaboAnalyst 5.0 software, developed by McGill University in Montreal, QC, Canada, is a comprehensive platform for metabolomics data analysis. The metabolite datasets were exported as CSV files and then imported into the program (16). The most distinctive characteristics within the group under study were chosen using MetaboAnalyst's sparse partial least squares-discriminant analysis (sPLS-DA) method for sample classification. By applying the false discovery rate (FDR) approach, multiple hypothesis testing corrections were applied with the goal of minimizing the rate of false positives. A two-tailed independent Student's t-test was used to determine whether the pregnant women with GDM had significantly different metabolites from the pregnant normoglycemic group. As a result, a volcano plot was made to show the fold change (p<0.05*, FC=1.25) and statistical significance, emphasizing the dysregulation of cellular metabolites for each condition. Metaboanalyst was utilized in the construction of Functional Enrichments. Furthermore, pathway analysis and metabolite set enrichment were conducted using MetaboAnalyst 5.0 (https://www.metaboanalyst.ca).", "Sodium formate was the calibrant used in the external calibration procedure. The two parts of the acquisition procedure were the auto MS scan, which took 0 to 0.3 minutes, and the auto MS/MS, which took 0.3 to 30 minutes and included fragmentation. At 12 Hz, both segments were run in the positive mode. The automatic in-run mass scan range was ±0.5 for the precursor ion width and covered 20–1300 m/z. For every cycle of 0.5 seconds, three precursors were selected, and 400 counts served as the threshold. Following three spectra, active exclusion was started, and it was lifted after 0.2 minutes.The MetaboScape® 4.0 software (Bruker Daltonics, Billerica, MA, USA) was used to analyze the collected data. The T-ReX 2D/3D workflow used bucketing parameters for the processed data, which included a peak length spanning 7 spectra, an intensity threshold of 1000, and peak area for quantification. Mass spectra calibration was performed in the range of 0-0.3 min, using features from at least 50 to 186 samples. With a mass range of 50 to 1300 m/z and a retention time range of 0.3 to 25 minutes, the auto MS/MS scan adhered to the average methodology. For the LC-QTOF analysis, duplicate samples from 52 individuals in each group were collected. Following the combination of these samples, a dataset with 3625 distinct features was produced. By matching the retention duration and MS/MS spectra to the HMBD 4.0 database—carefully designed to meet the unique requirements of the metabolomics community—metabolites were identified. After filtering with MetaboScape®, an extensive collection of 108 unique metabolites was selected. The quantitative data matrix was created using each metabolite''s peak intensities. The metabolite datasets contained only those metabolites that were statistically significant (p-value < 0.05*) and listed in the human metabolome database 4.0 (HMDB). The human metabolites were filtered using the HMDB online database (https://hmdb.ca/metabolites/HMDB0059911). There were still 108 distinct metabolites after HMDB filtration. The MetaboAnalyst 5.0 software, developed by McGill University in Montreal, QC, Canada, is a comprehensive platform for metabolomics data analysis. The metabolite datasets were exported as CSV files and then imported into the program (16). The most distinctive characteristics within the group under study were chosen using MetaboAnalyst''s sparse partial least squares-discriminant analysis (sPLS-DA) method for sample classification. By applying the false discovery rate (FDR) approach, multiple hypothesis testing corrections were applied with the goal of minimizing the rate of false positives. A two-tailed independent Student''s t-test was used to determine whether the pregnant women with GDM had significantly different metabolites from the pregnant normoglycemic group. As a result, a volcano plot was made to show the fold change (p<0.05*, FC=1.25) and statistical significance, emphasizing the dysregulation of cellular metabolites for each condition. Metaboanalyst was utilized in the construction of Functional Enrichments. Furthermore, pathway analysis and metabolite set enrichment were conducted using MetaboAnalyst 5.0 (https://www.metaboanalyst.ca)."]} 'Metabolites' section of 'MS_METABOLITE_DATA' block do not match. 'Data' section of 'MS_METABOLITE_DATA' block do not match.