2-Oxovaleric acid
(Synonyms: 2-戊酮酸) 目录号 : GC307132-Oxovalericacid是在人体血液中发现的一种酮酸。
Cas No.:1821-02-9
Sample solution is provided at 25 µL, 10mM.
Quality Control & SDS
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- Purity: >95.00%
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- SDS (Safety Data Sheet)
- Datasheet
2-Oxovaleric acid is a keto acid that is found in human blood.
Cas No. | 1821-02-9 | SDF | |
别名 | 2-戊酮酸 | ||
Canonical SMILES | CCCC(C(O)=O)=O | ||
分子式 | C5H8O3 | 分子量 | 116.12 |
溶解度 | DMSO: ≥ 250 mg/mL (2152.95 mM) | 储存条件 | Store at -20°C |
General tips | 请根据产品在不同溶剂中的溶解度选择合适的溶剂配制储备液;一旦配成溶液,请分装保存,避免反复冻融造成的产品失效。 储备液的保存方式和期限:-80°C 储存时,请在 6 个月内使用,-20°C 储存时,请在 1 个月内使用。 为了提高溶解度,请将管子加热至37℃,然后在超声波浴中震荡一段时间。 |
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Shipping Condition | 评估样品解决方案:配备蓝冰进行发货。所有其他可用尺寸:配备RT,或根据请求配备蓝冰。 |
制备储备液 | |||
1 mg | 5 mg | 10 mg | |
1 mM | 8.6118 mL | 43.0589 mL | 86.1178 mL |
5 mM | 1.7224 mL | 8.6118 mL | 17.2236 mL |
10 mM | 0.8612 mL | 4.3059 mL | 8.6118 mL |
第一步:请输入基本实验信息(考虑到实验过程中的损耗,建议多配一只动物的药量) | ||||||||||
给药剂量 | mg/kg | 动物平均体重 | g | 每只动物给药体积 | ul | 动物数量 | 只 | |||
第二步:请输入动物体内配方组成(配方适用于不溶于水的药物;不同批次药物配方比例不同,请联系GLPBIO为您提供正确的澄清溶液配方) | ||||||||||
% DMSO % % Tween 80 % saline | ||||||||||
计算重置 |
计算结果:
工作液浓度: mg/ml;
DMSO母液配制方法: mg 药物溶于 μL DMSO溶液(母液浓度 mg/mL,
体内配方配制方法:取 μL DMSO母液,加入 μL PEG300,混匀澄清后加入μL Tween 80,混匀澄清后加入 μL saline,混匀澄清。
1. 首先保证母液是澄清的;
2.
一定要按照顺序依次将溶剂加入,进行下一步操作之前必须保证上一步操作得到的是澄清的溶液,可采用涡旋、超声或水浴加热等物理方法助溶。
3. 以上所有助溶剂都可在 GlpBio 网站选购。
Metabolomics and a Breath Sensor Identify Acetone as a Biomarker for Heart Failure
Background: Multi-omics delivers more biological insight than targeted investigations. We applied multi-omics to patients with heart failure with reduced ejection fraction (HFrEF). Methods: 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography mass spectrometry (LC-MS/GC-MS) and solid-phase microextraction (SPME) volatilomics in plasma and urine. HFrEF was defined using left ventricular global longitudinal strain, ejection fraction and NTproBNP. A consumer breath acetone (BrACE) sensor validated results in n = 73. Results: 28 metabolites were identified by GCMS, 35 by LCMS and 4 volatiles by SPME in plasma and urine. Alanine, aspartate and glutamate, citric acid cycle, arginine biosynthesis, glyoxylate and dicarboxylate metabolism were altered in HFrEF. Plasma acetone correlated with NT-proBNP (r = 0.59, 95% CI 0.4 to 0.7), 2-oxovaleric and cis-aconitic acid, involved with ketone metabolism and mitochondrial energetics. BrACE > 1.5 ppm discriminated HF from other cardiac pathology (AUC 0.8, 95% CI 0.61 to 0.92, p < 0.0001). Conclusion: Breath acetone discriminated HFrEF from other cardiac pathology using a consumer sensor, but was not cardiac specific.
Plasma and urine metabolomic analyses in aortic valve stenosis reveal shared and biofluid-specific changes in metabolite levels
Aortic valve stenosis (AVS) is a prevalent condition among the elderly population that eventually requires aortic valve replacement. The lack of reliable biomarkers for AVS poses a challenge for its early diagnosis and the application of preventive measures. Untargeted gas chromatography mass spectrometry (GC-MS) metabolomics was applied in 46 AVS cases and 46 controls to identify plasma and urine metabolites underlying AVS risk. Multivariate data analyses were performed on pre-processed data (e.g. spectral peak alignment), in order to detect changes in metabolite levels in AVS patients and to evaluate their performance in group separation and sensitivity of AVS prediction, followed by regression analyses to test for their association with AVS. Through untargeted analysis of 190 urine and 130 plasma features that could be detected and quantified in the GC-MS spectra, we identified contrasting levels of 22 urine and 21 plasma features between AVS patients and control subjects. Following metabolite assignment, we observed significant changes in the concentration of known metabolites in urine (n = 14) and plasma (n = 15) that distinguish the metabolomic profiles of AVS patients from healthy controls. Associations with AVS were replicated in both plasma and urine for about half of these metabolites. Among these, 2-Oxovaleric acid, elaidic acid, myristic acid, palmitic acid, estrone, myo-inositol showed contrasting trends of regulation in the two biofluids. Only trans-Aconitic acid and 2,4-Di-tert-butylphenol showed consistent patterns of regulation in both plasma and urine. These results illustrate the power of metabolomics in identifying potential disease-associated biomarkers and provide a foundation for further studies towards early diagnostic applications in severe heart conditions that may prevent surgery in the elderly.