Comparison Log
2024-12-01 07:05:10.341818
mwtab Python Library Version: 1.2.5
Source:      https://www.metabolomicsworkbench.org/rest/study/analysis_id/AN005756/mwtab/...
Study ID:    ST003506
Analysis ID: AN005756
Status:      Inconsistent

Sections "STUDY" contain missmatched items: {('STUDY_SUMMARY', "Diagnosing and treating diseases such as breast cancer-related lymphedema (BCRL) is challenging due to a limited understanding of the underlying mechanisms. Despite recent advancements, BCRL significantly impacts patients' quality of life, as current treatments only manage symptoms. Leveraging modern high-throughput omics technologies, particularly metabolomics, holds potential to address these challenges. Metabolomics offers insights into dynamic changes influenced by internal and external factors, aiding in understanding the tissue physiology and detecting pathological conditions. The investigation of metabolomic biomarkers holds promise for early lymphedema diagnosis and personalized treatment. The deposited dataset represent high-resolution nuclear magnetic resonance (NMR) data for patients' blood serum and interstitial fluid, obtained after breast cancer treatment and with diagnosed BCRL, as well as control samples. Simple statistical analysis yielded increased concentrations of pyruvate, citrate, 2-ketoisovalerate, ketoleucine, 3-methyl-2-oxovalerate, tryptophan, and ascorbate in serum samples from patients with lymphedema. This dataset can aid in identifying early-stage lymphedema biomarkers and deepen insights into lymphatic system function thus leading to the development of effective diagnostic and therapeutic tools."), ('STUDY_SUMMARY', "Diagnosing and treating diseases such as breast cancer-related lymphedema (BCRL) is challenging due to a limited understanding of the underlying mechanisms. Despite recent advancements, BCRL significantly impacts patients'' quality of life, as current treatments only manage symptoms. Leveraging modern high-throughput omics technologies, particularly metabolomics, holds potential to address these challenges. Metabolomics offers insights into dynamic changes influenced by internal and external factors, aiding in understanding the tissue physiology and detecting pathological conditions. The investigation of metabolomic biomarkers holds promise for early lymphedema diagnosis and personalized treatment. The deposited dataset represent high-resolution nuclear magnetic resonance (NMR) data for patients'' blood serum and interstitial fluid, obtained after breast cancer treatment and with diagnosed BCRL, as well as control samples. Simple statistical analysis yielded increased concentrations of pyruvate, citrate, 2-ketoisovalerate, ketoleucine, 3-methyl-2-oxovalerate, tryptophan, and ascorbate in serum samples from patients with lymphedema. This dataset can aid in identifying early-stage lymphedema biomarkers and deepen insights into lymphatic system function thus leading to the development of effective diagnostic and therapeutic tools.")}
Sections "PROJECT" contain missmatched items: {('PROJECT_SUMMARY', "Diagnosing and treating diseases such as breast cancer-related lymphedema (BCRL) is challenging due to a limited understanding of the underlying mechanisms. Despite recent advancements, BCRL significantly impacts patients' quality of life, as current treatments only manage symptoms. Leveraging modern high-throughput omics technologies, particularly metabolomics, holds potential to address these challenges."), ('PROJECT_SUMMARY', "Diagnosing and treating diseases such as breast cancer-related lymphedema (BCRL) is challenging due to a limited understanding of the underlying mechanisms. Despite recent advancements, BCRL significantly impacts patients'' quality of life, as current treatments only manage symptoms. Leveraging modern high-throughput omics technologies, particularly metabolomics, holds potential to address these challenges.")}
Sections "COLLECTION" contain missmatched items: {('COLLECTION_SUMMARY', "The investigations were conducted in accordance with the principles outlined in the Declaration of Helsinki 2008 (https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/doh-oct2008/, accessed on 01.08.2024), which delineate ethical principles for medical research involving human subjects. Ethical approval was obtained from the International Tomography Center SB RAS ( ECITC-2020-12 from 16.12.2020) and the Research Institute of Clinical and Experimental Lymphology – Branch of the Institute of Cytology and Genetics SB RAS ( 159 from 20.07.2020). Written informed consent was obtained from all patients after a thorough explanation of the study''s nature and potential consequences. No special permission from national or local authorities was required. Blood (n=22) and interstitial fluid (n=9) samples were collected from patients who had previously undergone breast cancer treatment and who subsequently developed and were diagnosed with BCRL (n=22, age 69.6 ± 3.4 years). Control blood samples (n=12, age 63.7 ± 6.5 years) were collected from age-matched patients without diagnosed breast can-cer and without lymphedema. Peripheral blood was obtained from the ulnar vein in the morning after overnight fasting under aseptic conditions into a vacutainer containing a coagulation activator. Within 10-15 minutes of collection, the blood samples were centrifuged (3000×g, 10 min), and the plasma obtained was transferred into separate Eppendorf vials. We have developed a method for collecting interstitial fluid from the subcutaneous tissue of the affected area from the patients with limb lymphedema. For this purpose, an ultrasound examination of the affected limb was performed using a 5 MHz and 10 MHz linear array probe. Areas of interstitial fluid accumulation were identified and marked. At these areas, the skin was punctured with a 27G needle and the interstitial fluid that appeared at the puncture site was collected using a 22G plastic catheter and transferred into clean Eppendorf vials. The vials with biofluid samples were immediately frozen and stored at -70°C until analysis."), ('COLLECTION_SUMMARY', "The investigations were conducted in accordance with the principles outlined in the Declaration of Helsinki 2008 (https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/doh-oct2008/, accessed on 01.08.2024), which delineate ethical principles for medical research involving human subjects. Ethical approval was obtained from the International Tomography Center SB RAS ( ECITC-2020-12 from 16.12.2020) and the Research Institute of Clinical and Experimental Lymphology – Branch of the Institute of Cytology and Genetics SB RAS ( 159 from 20.07.2020). Written informed consent was obtained from all patients after a thorough explanation of the study's nature and potential consequences. No special permission from national or local authorities was required. Blood (n=22) and interstitial fluid (n=9) samples were collected from patients who had previously undergone breast cancer treatment and who subsequently developed and were diagnosed with BCRL (n=22, age 69.6 ± 3.4 years). Control blood samples (n=12, age 63.7 ± 6.5 years) were collected from age-matched patients without diagnosed breast can-cer and without lymphedema. Peripheral blood was obtained from the ulnar vein in the morning after overnight fasting under aseptic conditions into a vacutainer containing a coagulation activator. Within 10-15 minutes of collection, the blood samples were centrifuged (3000×g, 10 min), and the plasma obtained was transferred into separate Eppendorf vials. We have developed a method for collecting interstitial fluid from the subcutaneous tissue of the affected area from the patients with limb lymphedema. For this purpose, an ultrasound examination of the affected limb was performed using a 5 MHz and 10 MHz linear array probe. Areas of interstitial fluid accumulation were identified and marked. At these areas, the skin was punctured with a 27G needle and the interstitial fluid that appeared at the puncture site was collected using a 22G plastic catheter and transferred into clean Eppendorf vials. The vials with biofluid samples were immediately frozen and stored at -70°C until analysis.")}
Sections "SAMPLEPREP" contain missmatched items: {('SAMPLEPREP_SUMMARY', 'Sample Preparation For the preparation of extracts from blood serum for metabolomic measurements, we adhered to a standard protocol. Serum volume was 300 μl, ISF volume varied from 50 to 200 μl. Briefly, after thawing human blood plasma, we removed the clot from the vial, thus obtaining blood serum. Then, we added 300 μL of H2O, 600 μL of cold (-20 °C) methanol and 600 μL of cold (-20 °C) chloroform to 300 µL of serum. The mixture was then stirred on a shaker at +4 °C for 15 minutes, followed by incubation at -20 °C for 30 minutes. Subsequently, the mixture was centrifuged at 16,100×g for 30 minutes at +4 °C. Upon centrifugation, the mixture separated into two immiscible phases. The upper water-methanol phase was collected and vacuum dried overnight. NMR Measurements The dried extracts were dissolved in 600 µL of D2O containing 20 µM of DSS (sodium 4,4-dimethyl-4-silapentane-1-sulfonic acid) as an internal standard and 50 mM of deuterated phosphate buffer (pH 7.2). The 1H NMR measurements were conducted at the Center of Collective Use Mass Spectrometric Investigations SB RAS, using an AVANCE III HD 700 MHz NMR spectrometer (Bruker BioSpin, Rheinstetten, Germany). NMR spectra for each sample were acquired in a standard 5 mm glass NMR tube using a 5 mm TXI ATMA NMR probe. We used 70-degree detection pulse (pulse sequence name zg) and summed 64 free induction decay (FID) signals while maintaining the sample temperature at 25°C. For two ISF samples with volumes below 100 µl, we collected 128 FIDs to improve the signal-to-noise ratio. Prior to acquisition, low-power radiation was applied at the water resonance frequency to presaturate the water signal. To allow for the relaxation of all spins, a repetition time of 6 seconds was used between scans. Identification and quantification of metabolites in NMR data Metabolite identification was conducted by analyzing their NMR spectra, which were sourced from literature, databases (HMDB, METLIN, BMRB, and SpectraBase), an in-house NMR library (AMDB). Baseline processing, identification, and integration of spectral NMR peaks (quantification) were performed using MestReNova v12.0 (Mestrelab Research, A Coruna, Spain). In instances where NMR signal assignment was not straightforward, the identification of metabolites was verified by spiking extracts with commercially available standard compounds. Despite these efforts, several signals in the NMR spectra remained unassigned. The metabolite concentrations in samples were determined in µM by integrating NMR signals relative to the internal standard DSS, and then normalized to the biofluid volume. On average, 60-80 compounds were identified in samples. However, quantifying some compounds was unreliable due to weak signals or overlapping with other signals. The final table contains only 51 reliably identified and quantified metabolites. The raw data could contain more information on as yet unidentified metabolites.'), ('SAMPLEPREP_SUMMARY', 'Sample Preparation For the preparation of extracts from blood serum for metabolomic measurements, we adhered to a standard protocol. Serum volume was 300 μl, ISF volume varied from 50 to 200 μl. Briefly, after thawing human blood plasma, we removed the clot from the vial, thus obtaining blood serum. Then, we added 300 μL of H2O, 600 μL of cold (-20 °C) methanol and 600 μL of cold (-20 °C) chloroform to 300 µL of serum. The mixture was then stirred on a shaker at +4 °C for 15 minutes, followed by incubation at -20 °C for 30 minutes. Subsequently, the mixture was centrifuged at 16,100×g for 30 minutes at +4 °C. Upon centrifugation, the mixture separated into two immiscible phases. The upper water-methanol phase was collected and vacuum dried overnight. NMR Measurements The dried extracts were dissolved in 600 µL of D2O containing 20 µM of DSS (sodium 4,4-dimethyl-4-silapentane-1-sulfonic acid) as an internal standard and 50 mM of deuterated phosphate buffer (pH 7.2). The 1H NMR measurements were conducted at the Center of Collective Use "Mass Spectrometric Investigations" SB RAS, using an AVANCE III HD 700 MHz NMR spectrometer (Bruker BioSpin, Rheinstetten, Germany). NMR spectra for each sample were acquired in a standard 5 mm glass NMR tube using a 5 mm TXI ATMA NMR probe. We used 70-degree detection pulse (pulse sequence name zg) and summed 64 free induction decay (FID) signals while maintaining the sample temperature at 25°C. For two ISF samples with volumes below 100 µl, we collected 128 FIDs to improve the signal-to-noise ratio. Prior to acquisition, low-power radiation was applied at the water resonance frequency to presaturate the water signal. To allow for the relaxation of all spins, a repetition time of 6 seconds was used between scans. Identification and quantification of metabolites in NMR data Metabolite identification was conducted by analyzing their NMR spectra, which were sourced from literature, databases (HMDB, METLIN, BMRB, and SpectraBase), an in-house NMR library (AMDB). Baseline processing, identification, and integration of spectral NMR peaks (quantification) were performed using MestReNova v12.0 (Mestrelab Research, A Coruna, Spain). In instances where NMR signal assignment was not straightforward, the identification of metabolites was verified by spiking extracts with commercially available standard compounds. Despite these efforts, several signals in the NMR spectra remained unassigned. The metabolite concentrations in samples were determined in µM by integrating NMR signals relative to the internal standard DSS, and then normalized to the biofluid volume. On average, 60-80 compounds were identified in samples. However, quantifying some compounds was unreliable due to weak signals or overlapping with other signals. The final table contains only 51 reliably identified and quantified metabolites. The raw data could contain more information on as yet unidentified metabolites.')}