DLAC
目录号 : GC43484
A detergent used to solubilize membrane proteins
Cas No.:70803-56-4
Sample solution is provided at 25 µL, 10mM.
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Quality Control & SDS
- View current batch:
- Purity: >97.00%
- COA (Certificate Of Analysis)
- SDS (Safety Data Sheet)
- Datasheet
DLAC is a detergent synthesized from lactobionic acid. It can be used to solubilize membrane proteins and has a critical micelle concentration (CMC) of 1.3 mM.
| Cas No. | 70803-56-4 | SDF | |
| Canonical SMILES | O[C@H]([C@H]1O)[C@H](O[C@]([C@H](O)[C@@H](O)C(NCCCCCCCCCC)=O)([H])[C@@H](CO)O)O[C@@H]([C@@H]1O)CO | ||
| 分子式 | C22H43NO11 | 分子量 | 497.6 |
| 溶解度 | Soluble in DMSO | 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 | 2.0096 mL | 10.0482 mL | 20.0965 mL |
| 5 mM | 0.4019 mL | 2.0096 mL | 4.0193 mL |
| 10 mM | 0.201 mL | 1.0048 mL | 2.0096 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。
"Virtual" attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning
Eur J Nucl Med Mol Imaging 2022 Jul;49(9):3140-3149.PMID:35312837DOI:10.1007/s00259-022-05735-7.
Purpose: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely used for coronary artery disease (CAD) evaluation. Although attenuation correction is recommended to diminish image artifacts and improve diagnostic accuracy, approximately 3/4ths of clinical MPI worldwide remains non-attenuation-corrected (NAC). In this work, we propose a novel deep learning (DL) algorithm to provide "virtual" DL attenuation-corrected (DLAC) perfusion polar maps solely from NAC data without concurrent computed tomography (CT) imaging or additional scans. Methods: SPECT MPI studies (N = 11,532) with paired NAC and CTAC images were retrospectively identified. A convolutional neural network-based DL algorithm was developed and trained on half of the population to predict DLAC polar maps from NAC polar maps. Total perfusion deficit (TPD) was evaluated for all polar maps. TPDs from NAC and DLAC polar maps were compared to CTAC TPDs in linear regression analysis. Moreover, receiver-operating characteristic analysis was performed on NAC, CTAC, and DLAC TPDs to predict obstructive CAD as diagnosed from invasive coronary angiography. Results: DLAC TPDs exhibited significantly improved linear correlation (p < 0.001) with CTAC (R2 = 0.85) compared to NAC vs. CTAC (R2 = 0.68). The diagnostic performance of TPD was also improved with DLAC compared to NAC with an area under the curve (AUC) of 0.827 vs. 0.780 (p = 0.012) with no statistically significant difference between AUC for CTAC and DLAC. At 88% sensitivity, specificity was improved by 18.9% for DLAC and 25.6% for CTAC. Conclusions: The proposed DL algorithm provided attenuation correction comparable to CTAC without the need for additional scans. Compared to conventional NAC perfusion imaging, DLAC significantly improved diagnostic accuracy.
Recombination properties of P1 DLAC
J Bacteriol 1982 Oct;152(1):345-50.PMID:6811556DOI:10.1128/jb.152.1.345-350.1982.
The P1 DLAC prophage plasmid of Escherichia coli K-12 has been utilized as the recipient DNA substrate in experiments with lambda plac5 transduction and with Hfr and F' conjugation. The P1 DLAC plasmid does not recombine with lambda plac5 at the elevated levels seen for the F42lac plasmid. Recombination between lambda plac5 and P1 DLAC is essentially indistinguishable from recombination between lambda plac5 and a chromosomal lac gene in tems of both level of recombination and recombination pathway (RecBC, RecE, and RecF) dependence. The initiation of recombination between P1 DLAC and lac genes from an Hfr or F' donor is severalfold more efficient than it is for a recipient chromosomal lac gene.
Kinetic analysis and structural studies of a high-efficiency laccase from Cerrena sp. RSD1
FEBS Open Bio 2018 Jul 3;8(8):1230-1246.PMID:30087829DOI:10.1002/2211-5463.12459.
A high-efficiency laccase, DLAC, was isolated from Cerrena sp. RSD1. The kinetic studies indicate that DLAC is a diffusion-limited enzyme. The crystal structure of DLAC was determined to atomic resolution, and its overall structure shares high homology to monomeric laccases, but displays unique substrate-binding loops from those in other laccases. The substrate-binding residues with small side chain and the short substrate-binding loop IV broaden the substrate-binding cavity and may facilitate large substrate diffusion. Unlike highly glycosylated fungal laccases, the less-glycosylated DLAC contains one highly conserved glycosylation site at N432 and an unique glycosylation site at N468. The N-glycans stabilize the substrate-binding loops and the protein structure, and the first N-acetylglucosamine is crucial for the catalytic efficiency. Additionally, a fivefold increase in protein yield is achieved via the submerged culture method for industrial applications. Database: The atomic coordinates of the structure of DLAC from Cerrena sp. RSD1 and structural factors have been deposited in the RCSB Protein Data Bank (PDB ID: 5Z1X).
Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images
World J Gastroenterol 2016 Oct 21;22(39):8641-8657.PMID:27818583DOI:10.3748/wjg.v22.i39.8641.
A new feature extraction technique for the detection of lesions created from mucosal inflammations in Crohn's disease, based on wireless capsule endoscopy (WCE) images processing is presented here. More specifically, a novel filtering process, namely Hybrid Adaptive Filtering (HAF), was developed for efficient extraction of lesion-related structural/textural characteristics from WCE images, by employing Genetic Algorithms to the Curvelet-based representation of images. Additionally, Differential Lacunarity (DLAC) analysis was applied for feature extraction from the HAF-filtered images. The resulted scheme, namely HAF-DLac, incorporates support vector machines for robust lesion recognition performance. For the training and testing of HAF-DLac, an 800-image database was used, acquired from 13 patients who undertook WCE examinations, where the abnormal cases were grouped into mild and severe, according to the severity of the depicted lesion, for a more extensive evaluation of the performance. Experimental results, along with comparison with other related efforts, have shown that the HAF-DLac approach evidently outperforms them in the field of WCE image analysis for automated lesion detection, providing higher classification results, up to 93.8% (accuracy), 95.2% (sensitivity), 92.4% (specificity) and 92.6% (precision). The promising performance of HAF-DLac paves the way for a complete computer-aided diagnosis system that could support physicians' clinical practice.
Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies
Hum Brain Mapp 2020 Sep;41(13):3667-3679.PMID:32436261DOI:10.1002/hbm.25039.
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized from nonattenuation corrected PET (PET-nonAC) images in an end-to-end fashion using deep learning approaches (DLAC) is evaluated for various radiotracers used in molecular neuroimaging studies. One hundred eighty brain PET scans acquired using 18 F-FDG, 18 F-DOPA, 18 F-Flortaucipir (targeting tau pathology), and 18 F-Flutemetamol (targeting amyloid pathology) radiotracers (40 + 5, training/validation + external test, subjects for each radiotracer) were included. The PET data were reconstructed using CT-based AC (CTAC) to generate reference PET-CTAC and without AC to produce PET-nonAC images. A deep convolutional neural network was trained to generate PET attenuation corrected images (PET-DLAC) from PET-nonAC. The quantitative accuracy of this approach was investigated separately for each radiotracer considering the values obtained from PET-CTAC images as reference. A segmented AC map (PET-SegAC) containing soft-tissue and background air was also included in the evaluation. Quantitative analysis of PET images demonstrated superior performance of the DLAC approach compared to SegAC technique for all tracers. Despite the relatively low quantitative bias observed when using the DLAC approach, this approach appears vulnerable to outliers, resulting in noticeable local pseudo uptake and false cold regions. Direct AC in image-space using deep learning demonstrated quantitatively acceptable performance with less than 9% absolute SUV bias for the four different investigated neuroimaging radiotracers. However, this approach is vulnerable to outliers which result in large local quantitative bias.