Record Information |
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Version | 1.0 |
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Created at | 2022-09-03 08:48:03 UTC |
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Updated at | 2022-09-03 08:48:03 UTC |
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NP-MRD ID | NP0172029 |
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Secondary Accession Numbers | None |
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Natural Product Identification |
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Common Name | (1s,2s,4s,7r,9r,11s,13r,14s,15r,16s,17r)-15-(acetyloxy)-4,14-dihydroxy-2,14,17-trimethyl-3-oxo-11-{[(2s,3r,4s,5s,6r)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}-10-oxatetracyclo[7.7.1.0²,⁷.0¹³,¹⁷]heptadecan-16-yl 2h-1,3-benzodioxole-5-carboxylate |
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Description | Javanicinoside H belongs to the class of organic compounds known as hydrolyzable tannins. These are tannins with a structure characterized by either of the following models. In model 1, the structure contains galloyl units (in some cases, shikimic acid units) that are linked to diverse polyol carbohydrate-, catechin-, or triterpenoid units. In model 2, contains at least two galloyl units C-C coupled to each other, and do not contain a glycosidically linked catechin unit. (1s,2s,4s,7r,9r,11s,13r,14s,15r,16s,17r)-15-(acetyloxy)-4,14-dihydroxy-2,14,17-trimethyl-3-oxo-11-{[(2s,3r,4s,5s,6r)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}-10-oxatetracyclo[7.7.1.0²,⁷.0¹³,¹⁷]heptadecan-16-yl 2h-1,3-benzodioxole-5-carboxylate is found in Picrasma javanica. It was first documented in 2022 (PMID: 36057363). Based on a literature review a significant number of articles have been published on Javanicinoside H (PMID: 36057352) (PMID: 36057303) (PMID: 36057301) (PMID: 36057249) (PMID: 36057012) (PMID: 36057226). |
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Structure | CC(=O)O[C@@H]1[C@@H](OC(=O)C2=CC=C3OCOC3=C2)[C@H]2[C@@]3(C)[C@@H](C[C@H]4CC[C@H](O)C(=O)[C@]24C)O[C@H](C[C@H]3[C@]1(C)O)O[C@@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@H]1O InChI=1S/C35H46O16/c1-14(37)47-30-27(51-31(43)15-5-8-18-19(9-15)46-13-45-18)28-33(2)16(6-7-17(38)29(33)42)10-22-34(28,3)21(35(30,4)44)11-23(49-22)50-32-26(41)25(40)24(39)20(12-36)48-32/h5,8-9,16-17,20-28,30,32,36,38-41,44H,6-7,10-13H2,1-4H3/t16-,17+,20-,21-,22-,23+,24-,25+,26-,27+,28-,30-,32+,33+,34-,35+/m1/s1 |
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Synonyms | Not Available |
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Chemical Formula | C35H46O16 |
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Average Mass | 722.7370 Da |
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Monoisotopic Mass | 722.27859 Da |
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IUPAC Name | (1S,2S,4S,7R,9R,11S,13R,14S,15R,16S,17R)-15-(acetyloxy)-4,14-dihydroxy-2,14,17-trimethyl-3-oxo-11-{[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}-10-oxatetracyclo[7.7.1.0^{2,7}.0^{13,17}]heptadecan-16-yl 2H-1,3-benzodioxole-5-carboxylate |
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Traditional Name | (1S,2S,4S,7R,9R,11S,13R,14S,15R,16S,17R)-15-(acetyloxy)-4,14-dihydroxy-2,14,17-trimethyl-3-oxo-11-{[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxy}-10-oxatetracyclo[7.7.1.0^{2,7}.0^{13,17}]heptadecan-16-yl 2H-1,3-benzodioxole-5-carboxylate |
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CAS Registry Number | Not Available |
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SMILES | CC(=O)O[C@@H]1[C@@H](OC(=O)C2=CC=C3OCOC3=C2)[C@H]2[C@@]3(C)[C@@H](C[C@H]4CC[C@H](O)C(=O)[C@]24C)O[C@H](C[C@H]3[C@]1(C)O)O[C@@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@H]1O |
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InChI Identifier | InChI=1S/C35H46O16/c1-14(37)47-30-27(51-31(43)15-5-8-18-19(9-15)46-13-45-18)28-33(2)16(6-7-17(38)29(33)42)10-22-34(28,3)21(35(30,4)44)11-23(49-22)50-32-26(41)25(40)24(39)20(12-36)48-32/h5,8-9,16-17,20-28,30,32,36,38-41,44H,6-7,10-13H2,1-4H3/t16-,17+,20-,21-,22-,23+,24-,25+,26-,27+,28-,30-,32+,33+,34-,35+/m1/s1 |
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InChI Key | OWMQOHYLQGALRH-NZFPOBKMSA-N |
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Experimental Spectra |
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| Not Available | Predicted Spectra |
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| Spectrum Type | Description | Depositor ID | Depositor Organization | Depositor | Deposition Date | View |
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1D NMR | 13C NMR Spectrum (1D, 25 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 100 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 252 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 1000 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 50 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 200 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 75 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 300 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 101 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 400 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 126 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 500 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 151 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 600 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 176 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 700 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 201 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 800 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 13C NMR Spectrum (1D, 226 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum | 1D NMR | 1H NMR Spectrum (1D, 900 MHz, H2O, predicted) | Wishart Lab | Wishart Lab | David Wishart | 2021-06-20 | View Spectrum |
| Chemical Shift Submissions |
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| Not Available | Species |
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Species of Origin | |
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Chemical Taxonomy |
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Description | Belongs to the class of organic compounds known as hydrolyzable tannins. These are tannins with a structure characterized by either of the following models. In model 1, the structure contains galloyl units (in some cases, shikimic acid units) that are linked to diverse polyol carbohydrate-, catechin-, or triterpenoid units. In model 2, contains at least two galloyl units C-C coupled to each other, and do not contain a glycosidically linked catechin unit. |
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Kingdom | Organic compounds |
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Super Class | Phenylpropanoids and polyketides |
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Class | Tannins |
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Sub Class | Hydrolyzable tannins |
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Direct Parent | Hydrolyzable tannins |
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Alternative Parents | |
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Substituents | - Hydrolyzable tannin
- Quassinoid
- Naphthopyran
- Hexose monosaccharide
- Glycosyl compound
- O-glycosyl compound
- Naphthalene
- Benzodioxole
- Dicarboxylic acid or derivatives
- Benzenoid
- Monosaccharide
- Oxane
- Pyran
- Cyclic alcohol
- Tertiary alcohol
- Carboxylic acid ester
- Secondary alcohol
- Ketone
- Polyol
- Oxacycle
- Carboxylic acid derivative
- Acetal
- Organoheterocyclic compound
- Organic oxide
- Organooxygen compound
- Primary alcohol
- Hydrocarbon derivative
- Carbonyl group
- Alcohol
- Organic oxygen compound
- Aromatic heteropolycyclic compound
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Molecular Framework | Aromatic heteropolycyclic compounds |
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External Descriptors | Not Available |
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Physical Properties |
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State | Not Available |
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Experimental Properties | Property | Value | Reference |
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Melting Point | Not Available | Not Available | Boiling Point | Not Available | Not Available | Water Solubility | Not Available | Not Available | LogP | Not Available | Not Available |
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Predicted Properties | |
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General References | - de Oliveira ACV, de Morais FAP, Campanholi KDSS, Bidoia DL, Balbinot RB, Nakamura CV, Caetano W, Hioka N, Monteiro ODS, da Rocha CQ, Goncalves RS: Melanoma-targeted photodynamic therapy based on hypericin-loaded multifunctional P123-spermine/folate micelles. Photodiagnosis Photodyn Ther. 2022 Aug 31;40:103103. doi: 10.1016/j.pdpdt.2022.103103. [PubMed:36057363 ]
- Singh S, Bharadwaj T, Verma D, Dutta K: Valorization of phenol contaminated wastewater for lipid production by Rhodosporidium toruloides 9564(T). Chemosphere. 2022 Dec;308(Pt 2):136269. doi: 10.1016/j.chemosphere.2022.136269. Epub 2022 Aug 31. [PubMed:36057352 ]
- Zhao N, Elshareef H, Li B, Wang B, Jia Z, Zhou L, Liu Y, Sultan M, Dong R, Zhou Y: The efforts of China to combat air pollution during the period of 2015-2018: A case study assessing the environmental, health and economic benefits in the Beijing-Tianjin-Hebei and surrounding "2 + 26" regions. Sci Total Environ. 2022 Aug 31;853:158437. doi: 10.1016/j.scitotenv.2022.158437. [PubMed:36057303 ]
- Tan M, Sun Y, Gui J, Wang J, Chen X, Song W, Wu D: Distribution characteristics of microplastics in typical organic solid wastes and their biologically treated products. Sci Total Environ. 2022 Dec 15;852:158440. doi: 10.1016/j.scitotenv.2022.158440. Epub 2022 Aug 31. [PubMed:36057301 ]
- Balafif F, Faris M, Subagio EA, Bajamal AH, Kusumadewi A: Lumbar disc herniation in a 15-year-old girl: A case report. Int J Surg Case Rep. 2022 Sep;98:107560. doi: 10.1016/j.ijscr.2022.107560. Epub 2022 Aug 27. [PubMed:36057249 ]
- Watanabe M, Kato H, Katayama D, Soeda F, Matsunaga K, Watabe T, Tatsumi M, Shimosegawa E, Tomiyama N: Semiquantitative analysis using whole-body dynamic F-18 fluoro-2-deoxy-glucose-positron emission tomography to differentiate between benign and malignant lesions. Ann Nucl Med. 2022 Nov;36(11):951-963. doi: 10.1007/s12149-022-01784-y. Epub 2022 Sep 3. [PubMed:36057012 ]
- Lou J, Xu J, Zhang Y, Sun Y, Fang A, Liu J, Mur LAJ, Ji B: PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images. Comput Methods Programs Biomed. 2022 Oct;225:107095. doi: 10.1016/j.cmpb.2022.107095. Epub 2022 Aug 28. [PubMed:36057226 ]
- Mosavi AH, Mohammadzadeh A, Rathinasamy S, Zhang C, Reuter U, Levente K, Adeli H: Deep learning fuzzy immersion and invariance control for type-I diabetes. Comput Biol Med. 2022 Oct;149:105975. doi: 10.1016/j.compbiomed.2022.105975. Epub 2022 Aug 17. [PubMed:36057197 ]
- Zheng KL, Bor WL, Vernooij LM, Breet NC, Kelder JC, Hackeng CM, Kropman RHJ, Ten Berg JM, Noordzij PG: Postoperative myocardial injury and platelet reactivity in patients undergoing vascular surgery: The platelet reactivity and postoperative myocardial injury after major vascular surgery (PROMISE) study. Thromb Res. 2022 Oct;218:177-185. doi: 10.1016/j.thromres.2022.08.023. Epub 2022 Aug 28. [PubMed:36057168 ]
- Tadesse BT, Zhao G, Kempen P, Solem C: Consolidated Bioprocessing in a Dairy Setting horizontal line Concurrent Yoghurt Fermentation and Lactose Hydrolysis without Using Lactase Enzymes. J Agric Food Chem. 2022 Sep 21;70(37):11623-11630. doi: 10.1021/acs.jafc.2c04191. Epub 2022 Sep 3. [PubMed:36057098 ]
- Koretsky MJ, Brovman EY, Urman RD, Tsai MH, Cheney N: A machine learning approach to predicting early and late postoperative reintubation. J Clin Monit Comput. 2023 Apr;37(2):501-508. doi: 10.1007/s10877-022-00908-z. Epub 2022 Sep 3. [PubMed:36057069 ]
- V K, Natarajan KS: Gravimetric weight loss of steel in self-compacting concrete blended with wood ash and silica fume. Environ Sci Pollut Res Int. 2023 Jan;30(4):9483-9495. doi: 10.1007/s11356-022-22780-9. Epub 2022 Sep 3. [PubMed:36057061 ]
- Rahimian G, Shahini Shams Abadi M, Mirzaei Y, Hussein Mer A, Ahmadi R, Azadegan-Dehkordi F: Relationship between mucosal TNF-alpha expression and Th1, Th17, Th22 and Treg responses in Helicobacter pylori infection. AMB Express. 2022 Sep 3;12(1):113. doi: 10.1186/s13568-022-01456-0. [PubMed:36057049 ]
- Simoens C, Philippaert K, Wuyts C, Goscinny S, Van Hoeck E, Van Loco J, Billen J, de Hoon J, Ampe E, Vangoitsenhoven R, Mertens A, Vennekens R, Van der Schueren B: Pharmacokinetics of Oral Rebaudioside A in Patients with Type 2 Diabetes Mellitus and Its Effects on Glucose Homeostasis: A Placebo-Controlled Crossover Trial. Eur J Drug Metab Pharmacokinet. 2022 Nov;47(6):827-839. doi: 10.1007/s13318-022-00792-7. Epub 2022 Sep 3. [PubMed:36057030 ]
- Kim SA, Jong YC, Kang MS, Yu CJ: Antioxidation activity of molecular hydrogen via protoheme catalysis in vivo: an insight from ab initio calculations. J Mol Model. 2022 Sep 3;28(10):287. doi: 10.1007/s00894-022-05264-y. [PubMed:36057001 ]
- LOTUS database [Link]
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