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Kmeriol

$928

  • Brand : BIOFRON

  • Catalogue Number : BN-O0950

  • Specification : 98%(HPLC)

  • CAS number : 54306-10-4

  • Formula : C12H18O5

  • Molecular Weight : 242.27

  • PUBCHEM ID : 129913

  • Volume : 5mg

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Catalogue Number

BN-O0950

Analysis Method

HPLC,NMR,MS

Specification

98%(HPLC)

Storage

2-8°C

Molecular Weight

242.27

Appearance

Solid

Botanical Source

Structure Type

Phenylpropanoid

Category

Standards;Natural Pytochemical;API

SMILES

COC1=CC(=CC(=C1OC)OC)CC(CO)O

Synonyms

p-methoxytodadiol/3-(3,4,5-trimethoxy-phenyl)-propane-1,2-diol/3-(3,4,5-Trimethoxy-phenyl)-propan-1,2-diol

IUPAC Name

3-(3,4,5-trimethoxyphenyl)propane-1,2-diol

Density

Solubility

Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.

Flash Point

Boiling Point

Melting Point

InChl

InChI=1S/C12H18O5/c1-15-10-5-8(4-9(14)7-13)6-11(16-2)12(10)17-3/h5-6,9,13-14H,4,7H2,1-3H3

InChl Key

SNDFRVPKIVPGED-UHFFFAOYSA-N

WGK Germany

RID/ADR

HS Code Reference

2933990000

Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

Meta Tag

provides coniferyl ferulate(CAS#:54306-10-4) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

No Technical Documents Available For This Product.

PMID

27403530

Abstract

This paper describes the application of the syngeneic model of breast cancer (4T1) to the studies on a role of pulmonary alveolar macrophages in cancer metastasis. The 4T1 cells expressing GFP in combination with imaging and confocal microscopy are used to monitor tumor growth, track metastasizing tumor cells, and quantify the metastatic burden. These approaches are supplemented by digital histopathology that allows the automated and unbiased quantification of metastases. In this method the routinely prepared histological lung sections, which are stained with hematoxylin and eosin, are scanned and converted to the digital slides that are then analyzed by the self-trained pattern recognition software. In addition, we describe the flow cytometry approaches with the use of multiple cell surface markers to identify alveolar macrophages in the lungs. To determine impact of alveolar macrophages on metastases and antitumor immunity these cells are depleted with the clodronate-containing liposomes administrated intranasally to tumor-bearing mice. This approach leads to the specific and efficient depletion of this cell population as confirmed by flow cytometry. Tumor volumes and lung metastases are evaluated in mice depleted of alveolar macrophages, to determine the role of these cells in the metastatic progression of breast cancer.

KEYWORDS

Medicine, Issue 112, Premetastatic niche, alveolar macrophages, lung metastases, clodronate liposomes, breast cancer model, cancer biology

Title

Studying the Role of Alveolar Macrophages in Breast Cancer Metastasis

Author

Surya Kumari Vadrevu, 1 Sharad Sharma, 1 , 2 Navin Chintala, 1 , 3 Jalpa Patel, 1 Magdalena Karbowniczek, 1 and Maciej Markiewski 1

Publish date

2016;

PMID

25337994

Abstract

Farming and pesticide use have previously been linked to non-Hodgkin lymphoma (NHL), chronic lymphocytic leukemia (CLL) and multiple myeloma (MM). We evaluated agricultural use of specific insecticides, fungicides, and fumigants and risk of NHL and NHL-subtypes (including CLL and MM) in a U.S.-based prospective cohort of farmers and commercial pesticide applicators. A total of 523 cases occurred among 54,306 pesticide applicators from enrollment (1993-97) through December 31, 2011 in Iowa, and December 31, 2010 in North Carolina. Information on pesticide use, other agricultural exposures and other factors was obtained from questionnaires at enrollment and at follow-up approximately five years later (1999-2005). Information from questionnaires, monitoring, and the literature were used to create lifetime-days and intensity-weighted lifetime days of pesticide use, taking into account exposure-modifying factors. Poisson and polytomous models were used to calculate relative risks (RR) and 95% confidence intervals (CI) to evaluate associations between 26 pesticides and NHL and five NHL-subtypes, while adjusting for potential confounding factors. For total NHL, statistically significant positive exposure-response trends were seen with lindane and DDT. Terbufos was associated with total NHL in ever/never comparisons only. In subtype analyses, terbufos and DDT were associated with small cell lymphoma/chronic lymphocytic leukemia/marginal cell lymphoma, lindane and diazinon with follicular lymphoma, and permethrin with MM. However, tests of homogeneity did not show significant differences in exposure-response among NHL-subtypes for any pesticide. Because 26 pesticides were evaluated for their association with NHL and its subtypes, some chance finding could have occurred. Our results showed pesticides from different chemical and functional classes were associated with an excess risk of NHL and NHL subtypes, but not all members of any single class of pesticides were associated with an elevated risk of NHL or NHL subtypes. These findings are among the first to suggest links between DDT, lindane, permethrin, diazinon and terbufos with NHL subtypes.

Title

Non-Hodgkin Lymphoma Risk and Insecticide, Fungicide and Fumigant Use in the Agricultural Health Study

Author

Michael C. R. Alavanja, 1 , * Jonathan N. Hofmann, 1 Charles F. Lynch, 2 Cynthia J. Hines, 3 Kathryn H. Barry, 1 Joseph Barker, 4 Dennis W. Buckman, 4 Kent Thomas, 5 Dale P. Sandler, 6 Jane A. Hoppin, 6 Stella Koutros, 1 Gabriella Andreotti, 1 Jay H. Lubin, 1 Aaron Blair, 1 and Laura E. Beane Freeman 1 Suminori Akiba, Editor

Publish date

2014

PMID

27713752

Abstract

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale.

KEYWORDS

crop diseases, machine learning, deep learning, digital epidemiology

Title

Using Deep Learning for Image-Based Plant Disease Detection

Author

Sharada P. Mohanty,1,2,3 David P. Hughes,4,5,6 and Marcel Salathe1,2,3,*

Publish date

2016;


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