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5,6,7,8-Tetramethoxycoumarin

$1,120

  • Brand : BIOFRON

  • Catalogue Number : BN-O1584

  • Specification : 98%(HPLC)

  • CAS number : 56317-15-8

  • Formula : C13H14O6

  • Molecular Weight : 266.3

  • PUBCHEM ID : 151319

  • Volume : 5mg

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Catalogue Number

BN-O1584

Analysis Method

Specification

98%(HPLC)

Storage

-20℃

Molecular Weight

266.3

Appearance

Cryst.

Botanical Source

This product is isolated and purified from the herbs of Croton laevigatus

Structure Type

Category

SMILES

COC1=C(C(=C(C2=C1C=CC(=O)O2)OC)OC)OC

Synonyms

2H-1-Benzopyran-2-one, 5,6,7,8-tetramethoxy-/5,6,7,8-tetramethoxy-chromen-2-one/5,6,7,8-Tetramethoxycoumarin/W1278/5,6,7,8-Tetramethoxy-2H-chromen-2-one

IUPAC Name

Density

1.2±0.1 g/cm3

Solubility

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

Flash Point

202.3±28.8 °C

Boiling Point

448.9±45.0 °C at 760 mmHg

Melting Point

InChl

InChl Key

FEGDYUCKOYJQOZ-UHFFFAOYSA-N

WGK Germany

RID/ADR

HS Code Reference

Personal Projective Equipment

Correct Usage

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

Meta Tag

provides coniferyl ferulate(CAS#:56317-15-8) 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

27494614

Abstract

Background
A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data-large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources-all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.

Methods and Findings
Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson’s Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting.

Conclusions
Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.

Title

Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations

Author

Ivo D. Dinov,1,5,8,* Ben Heavner,2 Ming Tang,1 Gustavo Glusman,2 Kyle Chard,4 Mike Darcy,3 Ravi Madduri,4 Judy Pa,5 Cathie Spino,8 Carl Kesselman,3 Ian Foster,4 Eric W. Deutsch,2 Nathan D. Price,2 John D. Van Horn,5 Joseph Ames,5 Kristi Clark,5 Leroy Hood,2 Benjamin M. Hampstead,6,7 William Dauer,8 and Arthur W. Toga5

Publish date

2016;

PMID

32271850

Abstract

Background and aims
Because the sex difference in outcomes of fracture was incompletely understood, we evaluated the post-fracture complications and mortality of female and male patients.

Methods
We conducted a nationwide study of 498,586 fracture patients who received inpatient care using Taiwan’s National Health Insurance Research Database 2008-2013 claims data. Female and male fracture patients were selected for comparison by using a propensity-score matching procedure. Age, low income, types of fracture, fracture with surgery, several medical conditions, number of hospitalization and emergency visits were considered as potential confounding factors. Multivariate logistic regressions were used to calculate the adjusted odds ratios (OR), the 95% CI of post-fracture complications and 30-day in-hospital mortality differences between women and men.

Results
Male patients had a higher risk of post-fracture pneumonia (OR 1.96, 95% CI 1.83-2.11), acute renal failure (OR 1.85, 95% CI 1.60-2.15), deep wound infection (OR 1.63, 95% CI 1.51-1.77), stroke (OR 1.58, 95% CI 1.49-1.67), septicemia (OR 1.51, 95% CI 1.42-1.61), acute myocardial infarction (OR 1.38, 95% CI 1.09-1.75) and 30-day in-hospital mortality (OR 1.69, 95% CI 1.48-1.93) compared with female patients. However, a lower risk of post-fracture urinary tract infection (OR 0.69, 95% CI 0.65-0.72) was found in men than in women. Male patients also had longer hospital stays and higher medical expenditures due to fracture admission than did the female patients. Higher rates of post-fracture adverse events in male patients were noted in all age groups and all types of fractures.

Conclusion
We raised the possibility that male patients showed more complications and higher mortality rates after fracture admission compared with female patients, with the exception of urinary tract infections.

Title

Sex differences in fracture outcomes within Taiwan population: A nationwide matched study

Author

Fang-Pai Chou, Conceptualization, Investigation, Methodology, Writing - original draft, Writing - review & editing,1,2 Hung-Chi Chang, Conceptualization, Investigation, Methodology, Validation, Visualization, Writing - review & editing,1,2 Chun-Chieh Yeh, Conceptualization, Investigation, Methodology, Validation, Visualization, Writing - review & editing,3,4 Chih-Hsing Wu, Conceptualization, Investigation, Methodology, Validation, Visualization, Writing - review & editing,5 Yih-Giun Cherng, Conceptualization, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing - review & editing,1,2 Ta-Liang Chen, Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing - review & editing,#2,6,7 and Chien-Chang Liao, Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing2,7,8,9,10,* Yu Ru Kou, Editor

Publish date

2020;

PMID

24454075

Abstract

The asymmetric unit of the title compound, C44H44N2O6, contains two independent mol­ecules with similar conformations. The di­hydro­naphthalene ring systems are approximately planar [maximum deviations = 0.036 (2), 0.128 (2), 0.0.24 (2) and 0.075 (2) a]. The dihedral angle between two di­hydro­naphthalene ring systems is 83.37 (4)° in one mol­ecule and 88.99 (4)° in the other. The carbonyl O atom is linked with the adjacent hy­droxy and imino groups via intra­molecular O—H⋯O and N—H⋯O hydrogen bonds. In the crystal, mol­ecules are linked through O—H⋯O hydrogen bonds into layers parallel to (001), and adjacent layers are further stacked by π-π inter­actions between di­hydro­naphthalene and phenyl rings into a three-dimensional supra­molecular architecture. In the crystal, one of the isopropyl groups is disordered over two positions with an occupancy ratio of 0.684 (8):0.316 (8).

Title

2,2′-Bis{8-[(benzyl­amino)­methyl­idene]-1,6-dihy­droxy-5-isopropyl-3-methyl­naphthalen-7(8H)-one}

Author

Shukhrat M. Hakberdiev,a,* Samat A. Talipov,b Davranbek N. Dalimov,a and Bakhtiyar T. Ibragimovb

Publish date

2013 Nov 1;


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