Shipping to United States We Offer Worldwide Shipping
Login Wishlist

Platycodin D3

$896

  • Brand : BIOFRON

  • Catalogue Number : BD-P0722

  • Specification : 98.5%(HPLC&TLC)

  • CAS number : 67884-03-1

  • Formula : C63H102O33

  • Molecular Weight : 1387.48

  • PUBCHEM ID : 70698293

  • Volume : 25mg

Available on backorder

Quantity
Checkout Bulk Order?

Catalogue Number

BD-P0722

Analysis Method

HPLC,NMR,MS

Specification

98.5%(HPLC&TLC)

Storage

2-8°C

Molecular Weight

1387.48

Appearance

Powder

Botanical Source

Structure Type

Triterpenoids

Category

SMILES

CC1C(C(C(C(O1)OC2C(C(COC2OC(=O)C34CCC(CC3C5=CCC6C(C5(CC4O)C)(CCC7C6(CC(C(C7(CO)CO)OC8C(C(C(C(O8)COC9C(C(C(C(O9)CO)O)O)O)O)O)O)O)C)C)(C)C)O)O)O)O)OC1C(C(C(CO1)O)OC1C(C(CO1)(CO)O)O)O

Synonyms

[(2S,3R,4S,5S)-3-[(2S,3R,4S,5R,6S)-5-[(2S,3R,4S,5R)-4-[(2S,3R,4R)-3,4-dihydroxy-4-(hydroxymethyl)oxolan-2-yl]oxy-3,5-dihydroxyoxan-2-yl]oxy-3,4-dihydroxy-6-methyloxan-2-yl]oxy-4,5-dihydroxyoxan-2-yl] (4aR,5R,6aR,6aS,6bR,8aR,10R,11S,12aR,14bS)-5,11-dihydroxy-9,9-bis(hydroxymethyl)-2,2,6a,6b,12a-pentamethyl-10-[(2R,3R,4S,5S,6R)-3,4,5-trihydroxy-6-[[(2R,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxymethyl]oxan-2-yl]oxy-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydropicene-4a-carboxylate

IUPAC Name

[(2S,3R,4S,5S)-3-[(2S,3R,4S,5R,6S)-5-[(2S,3R,4S,5R)-4-[(2S,3R,4R)-3,4-dihydroxy-4-(hydroxymethyl)oxolan-2-yl]oxy-3,5-dihydroxyoxan-2-yl]oxy-3,4-dihydroxy-6-methyloxan-2-yl]oxy-4,5-dihydroxyoxan-2-yl] (4aR,5R,6aR,6aS,6bR,8aR,10R,11S,12aR,14bS)-5,11-dihydroxy-9,9-bis(hydroxymethyl)-2,2,6a,6b,12a-pentamethyl-10-[(2R,3R,4S,5S,6R)-3,4,5-trihydroxy-6-[[(2R,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxymethyl]oxan-2-yl]oxy-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydropicene-4a-carboxylate

Applications

Density

Solubility

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

Flash Point

Boiling Point

Melting Point

InChl

InChI=1S/C63H102O33/c1-24-45(92-51-44(81)46(29(70)18-85-51)93-55-48(82)62(84,22-67)23-88-55)40(77)43(80)52(89-24)94-47-35(72)28(69)17-86-54(47)96-56(83)63-12-11-57(2,3)13-26(63)25-7-8-32-58(4)14-27(68)49(61(20-65,21-66)33(58)9-10-59(32,5)60(25,6)15-34(63)71)95-53-42(79)39(76)37(74)31(91-53)19-87-50-41(78)38(75)36(73)30(16-64)90-50/h7,24,26-55,64-82,84H,8-23H2,1-6H3/t24-,26-,27-,28-,29+,30+,31+,32+,33+,34+,35-,36+,37+,38-,39-,40-,41+,42+,43+,44+,45-,46-,47+,48-,49-,50+,51-,52-,53-,54-,55-,58+,59+,60+,62+,63+/m0/s1

InChl Key

XHKCYIRZWRRXNG-COMVGSAYSA-N

WGK Germany

RID/ADR

HS Code Reference

2932990000

Personal Projective Equipment

Correct Usage

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

Meta Tag

provides coniferyl ferulate(CAS#:67884-03-1) 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

30822259

Abstract

Purpose: We aimed to estimate latent classes of concurrent polysubstance use and test for sexual orientation differences in latent class memberships with representative data from adolescents living in 19 U.S. states. We also tested whether sex, race/ethnicity, and age moderated the sexual identity differences in polysubstance use class memberships.

Methods: We analyzed data from 119,437 adolescents from 19 states who participated in the 2015 Youth Risk Behavior Survey. Latent class analysis characterized polysubstance use patterns based on self-reported frequency of lifetime and past-month use of alcohol (including heavy episodic drinking), tobacco (cigarettes, cigars, and smokeless tobacco), and marijuana. Multinomial logistic regression models tested differences in latent class memberships by sexual identity. Interaction terms tested whether sex, race/ethnicity, and age moderated the sexual identity differences in polysubstance use class memberships.

Results: A six-class model of polysubstance use fit the data best and included nonusers (61.5%), experimental users (12.2%), marijuana-alcohol users (14.8%), tobacco-alcohol users (3.8%), medium-frequency three-substance users (3.6%), and high-frequency three-substance users (4.1%). Gay/lesbian- and bisexual-identified adolescents had significantly higher odds than heterosexual-identified adolescents of being in all of the user classes compared with the nonuser class. These sexual identity differences in latent polysubstance use class memberships were generally larger for females than for males, varied occasionally by race/ethnicity, and were sometimes larger for younger ages.

Conclusion: Compared with their heterosexual peers, gay/lesbian and bisexual adolescents—especially females—are at heightened risk of engaging in multiple types of polysubstance use. Designing, implementing, and evaluating interventions will likely reduce these sexual orientation disparities.

KEYWORDS

adolescents, polysubstance use, sex, sexual minority youth, sexual orientation, YRBS

Title

Latent Classes of Polysubstance Use Among Adolescents in the United States: Intersections of Sexual Identity with Sex, Age, and Race/Ethnicity

Author

Robert W.S. Coulter, PhD, MPH,1,,2,,3,,4 Deanna Ware, MPH,5 Jessica N. Fish, PhD,6 and Michael W. Plankey, PhD5

Publish date

2019 Apr 1

PMID

30600629

Abstract

The validity of the classification of non‐affective and affective psychoses as distinct entities has been disputed, but, despite calls for alternative approaches to defining psychosis syndromes, there is a dearth of empirical efforts to identify transdiagnostic phenotypes of psychosis. We aimed to investigate the validity and utility of general and specific symptom dimensions of psychosis cutting across schizophrenia, schizoaffective disorder and bipolar I disorder with psychosis. Multidimensional item‐response modeling was conducted on symptom ratings of the Positive and Negative Syndrome Scale, Young Mania Rating Scale, and Montgomery‐asberg Depression Rating Scale in the multicentre Bipolar‐Schizophrenia Network on Intermediate Phenotypes (B‐SNIP) consortium, which included 933 patients with a diagnosis of schizophrenia (N=397), schizoaffective disorder (N=224), or bipolar I disorder with psychosis (N=312). A bifactor model with one general symptom dimension, two distinct dimensions of non‐affective and affective psychosis, and five specific symptom dimensions of positive, negative, disorganized, manic and depressive symptoms provided the best model fit. There was further evidence on the utility of symptom dimensions for predicting B‐SNIP psychosis biotypes with greater accuracy than categorical DSM diagnoses. General, positive, negative and disorganized symptom dimension scores were higher in African American vs. Caucasian patients. Symptom dimensions accurately classified patients into categorical DSM diagnoses. This study provides evidence on the validity and utility of transdiagnostic symptom dimensions of psychosis that transcend traditional diagnostic boundaries of psychotic disorders. Findings further show promising avenues for research at the interface of dimensional psychopathological phenotypes and basic neurobiological dimensions of psychopathology.

KEYWORDS

Psychosis, transdiagnostic phenotypes, schizophrenia, schizoaffective disorder, bipolar disorder with psychosis, general symptom dimensions, specific symptom dimensions, biotypes

Title

Transdiagnostic dimensions of psychosis in the Bipolar‐Schizophrenia Network on Intermediate Phenotypes (B‐SNIP)

Author

Ulrich Reininghaus, 1 , 2 , 3 Jan R. Bohnke, 4 , 5 UnYoung Chavez‐Baldini, 2 Robert Gibbons, 6 Elena Ivleva, 7 Brett A. Clementz, 8 Godfrey D. Pearlson, 9 Matcheri S. Keshavan, 10 John A. Sweeney, 11 and Carol A. Tamminga 7

Publish date

2019 Feb;

PMID

32632105

Abstract

Endurance athlete performance is greatly dependent on sufficient post-race system recovery, as endurance races have substantial physiological, immunological and metabolic effects on these athletes. To date, the effects of numerous recovery modalities have been investigated, however, very limited literature exists pertaining to metabolic recovery of athletes after endurance races without the utilisation of recovery modalities. As such, this investigation is aimed at identifying the metabolic recovery trend of athletes within 48 h after a marathon. Serum samples of 16 athletes collected 24 h before, immediately after, as well as 24 h and 48 h post-marathon were analysed using an untargeted two-dimensional gas chromatography time-of-flight mass spectrometry metabolomics approach. The metabolic profiles of these comparative time-points indicated a metabolic shift from the overall post-marathon perturbed state back to the pre-marathon metabolic state during the recovery period. Statistical analyses of the data identified 61 significantly altered metabolites including amino acids, fatty acids, tricarboxylic acid cycle, carbohydrates and associated intermediates. These intermediates recovered to pre-marathon related concentrations within 24 h post-marathon, except for xylose which only recovered within 48 h. Furthermore, fluctuations in cholesterol and pyrimidine intermediates indicated the activation of alternative recovery mechanisms. Metabolic recovery of the athletes was attained within 48 h post-marathon, most likely due to reduced need for fuel substrate catabolism. This may result in the activation of glycogenesis, uridine-dependent nucleotide synthesis, protein synthesis, and the inactivation of cellular autophagy. These results may be beneficial in identifying more efficient, targeted recovery approaches to improve athletic performance.

Subject terms: Metabolomics, Metabolomics

Title

The unaided recovery of marathon-induced serum metabolome alterations

Author

Zinandre Stander,1 Laneke Luies,1 Lodewyk J. Mienie,1 Mari Van Reenen,1 Glyn Howatson,2,3 Karen M. Keane,2 Tom Clifford,4,5 Emma J. Stevenson,4 and Du Toit Lootscorresponding author1,6

Publish date

2020;