DDA
(Synonyms: (2E,4E)-癸-2,4-二烯醛) 目录号 : GC40697
A lipid decomposition product with cancer inhibiting activity
Cas No.:25152-84-5
Sample solution is provided at 25 µL, 10mM.
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Quality Control & SDS
- View current batch:
- Purity: >98.00%
- COA (Certificate Of Analysis)
- SDS (Safety Data Sheet)
- Datasheet
Lipoxygenase-catalyzed peroxidative decomposition of unsaturated fatty acids occurs within seconds when diatoms are crushed or eaten, producing alkyls. DDA is a prominent member of this class of reactive compounds. Common ω-6 fatty acids such as linoleic acid, dihomo-ω-linolenic acid, and arachidonic acid can give rise to DDA. DDA reduces the hatching rate and has a strong teratogenic effect on the eggs of pelagic copepods, at concentrations around 1 µM. In human carcinoma Caco2 cells, DDA induces cell growth arrest at around 15 µM. DDA appears to be a natural defensive chemical designed to limit the reproductive success of copepods, the main predators of diatoms. It may also be a more general inducer of apoptosis.
| Cas No. | 25152-84-5 | SDF | |
| 别名 | (2E,4E)-癸-2,4-二烯醛 | ||
| Canonical SMILES | CCCCC\C=C\C=C/C=O | ||
| 分子式 | C10H16O | 分子量 | 152.2 |
| 溶解度 | DMF: 10 mg/ml,DMSO: 10 mg/ml,Ethanol: 10 mg/ml,Ethanol:PBS (pH 7.2) (1:2): .15 mg/ml | 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 | |||
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1 mg | 5 mg | 10 mg |
| 1 mM | 6.5703 mL | 32.8515 mL | 65.703 mL |
| 5 mM | 1.3141 mL | 6.5703 mL | 13.1406 mL |
| 10 mM | 0.657 mL | 3.2852 mL | 6.5703 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。
DDA as an immunological adjuvant
Res Immunol 1992 Jun;143(5):494-503; discussion 574-6.PMID:1439129DOI:10.1016/0923-2494(92)80060-x.
As compared to other adjuvants, DDA is a moderate or strong adjuvant for humoral responses and a strong adjuvant for CMI, especially DTH responses, against different types of antigens and in both laboratory animals and larger animals. DDA can collaborate with other immunomodulating compounds resulting in further enhanced responses. Mechanisms include interactions with both antigen and components of the host immune system and possibly, multiple beneficial effects contribute to the relatively strong adjuvanticity of DDA. Toxicity of DDA is not known but severe detrimental side effects were not seen. This adjuvant can be applied in experimental vaccines and in commercial vaccines for veterinary purposes, especially if cell-mediated immunity is considered to be important. In immunology, DDA can be of use to study T helper cells responsible for DTH responses (T helper cells type 1) and to characterize T helper cell epitopes on antigens (Snijder et al., 1992).
Proteomic datasets of HeLa and SiHa cell lines acquired by DDA-PASEF and diaPASEF
Data Brief 2022 Feb 4;41:107919.PMID:35198691DOI:10.1016/j.dib.2022.107919.
We present four datasets on proteomics profiling of HeLa and SiHa cell lines associated with the research described in the paper "PROTREC: A probability-based approach for recovering missing proteins based on biological networks" [1]. Proteins in each cell line were acquired by two different data acquisition methods. The first was Data Dependent Acquisition-Parallel Accumulation Serial Fragmentation (DDA-PASEF) and the second was Parallel Accumulation-Serial Fragmentation combined with data-independent acquisition (diaPASEF) [2], [3]. Protein assembly was performed following search against the Swiss-Prot Human database using Peaks Studio for DDA datasets and Spectronaut for DIA datasets. The assembled result contains identified PSMs, peptides and proteins that are above threshold for each HeLa and SiHa sample. Coverage-wise, for DDA-PASEF, approximately 6,090 and 7,298 proteins were quantified for HeLa and SiHA sample, while13,339 and 8,773 proteins were quantified by diaPASEF for HeLa for SiHa sample, respectively. Consistency-wise, diaPASEF has fewer missing values (∼ 2%) compared to its DDA counterparts (∼5-7%). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository [4] with the dataset identifier PXD029773.
Self-Assembly of NaOL-DDA Mixtures in Aqueous Solution: A Molecular Dynamics Simulation Study
Molecules 2021 Nov 24;26(23):7117.PMID:34885699DOI:10.3390/molecules26237117.
The self-assembly behaviors of sodium oleate (NaOL), dodecylamine (DDA), and their mixtures in aqueous solution were systematically investigated by large-scale molecular dynamics simulations, respectively. The interaction mechanisms between the surfactants, as well as the surfactants and solvent, were revealed via the radial distribution function (RDF), cluster size, solvent-accessible surface area (SASA), hydrogen bond, and non-bond interaction energy. Results showed that the molecules more easily formed aggregates in mixed systems compared to pure systems, indicating higher surface activity. The SASA values of DDA and NaOL decreased significantly after mixing, indicating a tighter aggregation of the mixed surfactants. The RDF results indicated that DDA and NaOL strongly interacted with each other, especially in the mixed system with a 1:1 molar ratio. Compared to van der Waals interactions, electrostatic interactions between the surfactant molecules were the main contributors to the improved aggregation in the mixed systems. Besides, hydrogen bonds were found between NaOL and DDA in the mixed systems. Therefore, the aggregates in the mixed systems were much more compact in comparison with pure systems, which contributed to the reduction of the repulsive force between same molecules. These findings indicated that the mixed NaOL/DDA surfactants had a great potential in application of mineral flotation.
AntDAS-DDA: A New Platform for Data-Dependent Acquisition Mode-Based Untargeted Metabolomic Profiling Analysis with Advantage of Recognizing Insource Fragment Ions to Improve Compound Identification
Anal Chem 2023 Jan 17;95(2):638-649.PMID:36599407DOI:10.1021/acs.analchem.2c01795.
Data-dependent acquisition (DDA) mode in ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) can provide massive amounts of MS1 and MS/MS information of compounds in untargeted metabolomics and can thus facilitate compound identification greatly. In this work, we developed a new platform called AntDAS-DDA for the automatic processing of UHPLC-HRMS data sets acquired under the DDA mode. Several algorithms, including extracted ion chromatogram extraction, feature extraction, MS/MS spectrum construction, fragment ion identification, and MS1 spectrum construction, were developed within the platform. The performance of AntDAS-DDA was investigated comprehensively with a mixture of standard and complex plant data sets. Results suggested that features in complex sample matrices can be extracted effectively, and the constructed MS1 and MS/MS spectra can benefit in compound identification greatly. The efficiency of compound identification can be improved by about 20%. AntDAS-DDA can take full advantage of MS/MS information in multiple sample analyses and provide more MS/MS spectra than single sample analysis. A comparison with advanced data analysis tools indicated that AntDAS-DDA may be used as an alternative for routine UHPLC-HRMS-based untargeted metabolomics. AntDAS-DDA is freely available at http://www.pmdb.org.cn/antdasdda.
Guidelines to the United Kingdom Disability Discrimination Act (DDA) 1995 and the Special Educational Needs and Disability Act (SENDA) 2001 with regard to nurse education and dyslexia
Nurse Educ Today 2005 Oct;25(7):542-9.PMID:16043268DOI:10.1016/j.nedt.2005.05.006.
This paper concerns the impact of disability legislation on nurse education, nurse educators and student nurses, in relation to academic work and clinical placement, with regard to dyslexia. The two United Kingdom acts considered are the Disability Discrimination Act (DDA), 1995 and the Special Educational Needs and Disability Act (SENDA), 2001, which is an amendment to the DDA. The paper examines and defines the main points of the acts, such as discrimination; less favourable treatment and its justification; reasonable adjustments; making adjustments in advance; disclosure and confidentiality requests; substantial disadvantage; current systems and regulations and concludes by raising issues which require clarification.