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Glycosides

Glycosides are a class of chemical compounds consisting of a sugar molecule (glycone) bonded to a non-sugar molecule (aglycone).
They are widely found in plants and play important roles in various biological processes.
Glycosides can exhibit diverse pharmacological properties, including anti-inflammatory, antimicrobial, and cardioactive effects.
Reseraching glycosides is crucial for developing new therapeutic agents and understanding their physiological functions.
The PubCompare.ai platform can streamlien glycoside reserach by helping scientists locate the most effective and reproducible protocols from literature, preprints, and patents, and analyze them using AI-driven tools to optimize their glycoside studies and achieve more reliable results.

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Glycan Reader has been integrated into the CHARMM-GUI web interface.19 (link) The user can either specify the PDB ID or upload the PDB structure into the server to generate the carbohydrate or protein/carbohydrate complex structure. If a carbohydrate is detected, then the graphical representation of the carbohydrate chain sequence will be displayed and the user can select the carbohydrate chains that they want to initialize in CHARMM (see Fig. 4). CHARMM allows modification in chemical structures, e.g., disulfide bond formation or phosphorylation using patch residues, and glycosidic linkages are generated using specific patch residues in CHARMM. The Glycan Reader web interface assigns the proper patches for glycosidic linkages and generates the CHARMM protein structure file (PSF) and coordinate files in both the PDB format and the CHARMM-specific coordinate format (CRD).
Currently, there are various patch residues available in the CHARMM carbohydrate force field to cover a range of carbohydrates including the majority found in eukaryotes.25 For example, O-methyl-, octyl-, dodecyl-, phosphate, and sulfate groups can be added to the reducing end of a sugar, and those modifications are properly patched in the PSF generation step (see Table 2 for the complete list of patch residues available). However, other types of common derivatizations, such as deoxidation, are not available, and, in such cases, the basic form of the carbohydrate molecule is used without modification and Glycan Reader informs the user. For example, if a user uploaded a structure of 2-deoxy glucose, a glucose molecule will be generated instead.
Publication 2011
2-Deoxyglucose Carbohydrates Carbohydrate Sequence Disulfides Eukaryota Glucose Glycosides Patched Receptors Phosphates Phosphorylation Polysaccharides Proteins Sugars Sulfates, Inorganic
The anomeric position of each carbohydrate monomer is examined to check if the residue is connected to another carbohydrate by the glycosidic linkage. In our scheme, the root residue of a carbohydrate chain is simply assigned to a residue that has a free reducing end: for example, α-D-N-acetyl-glucose in Fig. 2D. N- or O-glycosylation is determined by cross-referencing the connected protein residue on the reducing end of the glycan chain; N-glycosylated when the reducing end is connected to ASN and O-glycosylated when the reducing end is connected to THR or SER. During the implementation, we frequently found incorrectly assigned bonds in glycan chains, which interfere with glycosidic linkage detection. For example, Figure 3A and 3B show incorrectly assigned bonds between neighboring residues possibly due to close proximity between two atoms, which forms a small ring structure and hinders the correct glycosidic linkage assignment. To assign glycosidic linkages reliably, each glycosidic linkage is reexamined to remove any chemical bonds that do not make chemical sense, e.g., oxygen atoms having three covalent bonds. On the other hand, there are some glycan chains that have missing glycosidic linkages (Fig. 3C). In such cases, Glycan Reader examines the distance between the anomeric carbon and the exocyclic oxygen on the neighboring residue; if it is in close proximity (e.g., < 2.5 Å), a glycosidic linkage is generated between the two residues. In rare occasions, covalent bonds with extreme bond lengths are present in the PDB (Fig. 3D); any chemical bonds that are longer than 5 Å will be removed in Glycan Reader. While these error correction features have been tested on a number of internal test cases, users are always advised to make sure that the input structure is correct and the output from the Glycan Reader is as intended. In the case that a carbohydrate chain is connected to a non-carbohydrate molecule, the entire chain is ignored presently. For instance, PDB:1S0J contains a ligand molecule that is a derivative of sialic acid with a methylumbelliferyl moiety, and Glycan Reader classifies the molecule as a non-carbohydrate molecule. While currently not implemented in an automated fashion, the potential to treat such moieties using the CHARMM General Force Field is possible.24 (link)The CHARMM carbohydrate force field20 (link),21 (link) provides several linkage types for mixed pyranose and furanose compounds, such as sucrose, lactulose, melezitose, raffinose, kestose, 6-kestose, isomaltulose, planteose, and nystose. This is because it is not possible to use the same linkage type between pyranose and furanose due to different atom types. Therefore, Glycan Reader detects the presence of mixed pyranose and furanose compounds, and uses appropriate linkage types to make glycosidic linkages between the pyranose and furanose residues.
Publication 2011
6-kestose Carbohydrates Carbon Glucose Glycosides isomaltulose Lactulose Ligands melezitose N-Acetylneuraminic Acid nystose Oxygen Plant Roots Polysaccharides Protein Glycosylation Proteins Raffinose Sucrose
Backbone parameters comprise the single bond torsions along the phosphodiester chain and the conformation of the sugar ring. In a conventional DNA strand, the backbone segment associated with each nucleotide (in the 5′→3′ direction) is described by the torsions α (03′-P-O5′-C5′), β (P-O5′-C5′-C4′), γ (O5′-C5′-C4′-C3′), δ (C5′-C4′-C3′-O3′), ϵ (C4′-C3′-O3′-P) and ζ (C3′-O3′-P-O5′), to which we must add the glycosidic angle χ (O4′-C1′-N1-C2 for pyrimidines and O4′-C1′-N9-C4 for purines) joining the sugar to the base and the ribose OH torsion (C1′-C2′-O2′-H2′) in the case of RNA.
We remark that calculating averages and standard deviations of angular variables is not trivial, unless they cover restricted angular ranges. There is also no simple definition of maximal and minimal values. This problem occurs in many branches of science with broadly distributed angular variables, for example, in analysing wind directions (30 ). While angular helical variables generally lie within limited ranges, backbone dihedrals can easily span the full range of 360°. In this case, maximal and minimal values in the Curves+ analysis are replaced with the parameter ‘range’ and angular averages and standard deviations are calculated using a vectorial approach. Range is defined as the number of 1° bins visited by a given variable in the interval 0–360°. This gives a good idea of the angular spread of variables. Note that when analysing molecular dynamics trajectories, this value may increase with sampling, giving an indication that more sampling probably needs to be done. However, the details of the angular distribution can be checked using the histogram output option of the supplementary program Canal (see below). For averages, angles are added as vectors in 2D space (with an angle θ having components x = Cos θ and y = Sin θ). The result is converted to a unit vector, whose X and Y components yield the average. Other approaches require assuming that the angles obey a presupposed type of distribution. We have checked our values against one such model (31 ), and found negligible differences for standard deviations up to roughly 20°. Larger values differ more significantly (5–10°), but in these cases it is the qualitative result that the variables in question fluctuate very strongly that is the most important.
The sugar ring is usefully described using pseudorotation parameters. Although strictly speaking there are four pseudorotation parameters for a five-membered ring (32 (link)), only two of these, the so-called phase (Pha) and amplitude (Amp), are generally useful. While the amplitude describes the degree of ring puckering, the phase describes which atoms are most displaced from the mean ring plane. We calculate these parameters using the formulae given below (33 ), which have the advantage of treating the ring dihedrals ν1 (C1′-C2′-C3′-C4′) to ν5 (O4′-C1′-C2′-C3′) in an equivalent manner. In this approach:

where and b =−0.4 note, if then .
Conventionally, sugar ring puckers are divided into 10 families described by the atom which is most displaced from the mean ring plane (C1′, C2′, C3′, C4′ or O4′) and the direction of this displacement (endo for displacements on the side of the C5′ atom and exo for displacements on the other side). These pucker families can be easily calculated from the phase angle and are also output by the Curves+ program.
In order to deal with non-standard nucleic acids the backbone parameters are not hard-wired into the program, but are contained in a data file (standard_s.lib) which can be modified or extended by this user. This makes it easy to analyse chemically modified backbones such as those, for example, in PNA (34 (link)).
Publication 2009
Carbohydrates Cloning Vectors Displacement, Psychology Endometriosis Glycosides Helix (Snails) Molecular Dynamics Nucleic Acids Nucleotides Pulp Canals purine Pyrimidines Ribose single bond Vertebral Column Wind
All of the 100 molecules obtained from the DGSA calculations were subjected to NOE-restrained simulated annealing refinement in XPLOR (47 ) with a distance-dependent dielectric constant, as described previously (39 (link),43 (link)). The force constants were scaled at 10–30 and 80–100 kcal mol−1 Å−2 for NOE and hydrogen bond distance restraints, respectively. A total of 727 NOE distance restraints, of which 324 are from inter-residue NOEs, were incorporated into the NOE-restrained structure calculation.
Dihedral angle restraints were used to restrict the glycosidic torsion angle (χ) for the experimentally assigned syn and anti conformations. A dihedral angle restraint of 60(±35)° was applied to the syn G-tetrad guanines, and a dihedral angle restraint of 240(±40)° was applied to the anti G-tetrad guanines. The force constants of dihedral angle restraints were 10 kcal · mol−1 · rad−2.
NOE-restrained simulated annealing refinement calculations were performed as described previously (39 (link),43 (link)). The time steps for all processes of heating, cooling and equilibration were set to 1 fs. The 10 best molecules were selected based both on their minimal energy terms and number of NOE violations and have been deposited in the Protein Data Bank (PDB ID 2JPZ).
Publication 2007
Glycosides Guanine-Tetrads Hydrogen Bonds
To annotate glycans in a given PDB file, Glycan Reader uses an algorithm that can detect carbohydrate-like molecules and assign correct carbohydrate types based on their molecular topology and 3D structures. The overall scheme in Glycan Reader is shown in Figure 1 and illustrated in Figure 2. Molecular topologies are built based on the HETATM records and CONECT records in a PDB file, and molecules that do not have carbohydrate-like topology are not considered. The chemical groups that are attached to the carbohydrate-like molecules are then examined to assign the correct carbohydrate type. Once the monomeric units are identified, glycosidic linkage types are determined.
Publication 2011
Carbohydrates Glycosides Polysaccharides

Most recents protocols related to «Glycosides»

A total of 211 compound classes were identified in the positive and negative ion modes. To visualize the compound class diversity, a sunburst plot was conducted (Fig. 4). The most prominently detected classes overall were carboxylic acids and derivatives (mainly due to amino acids, peptides, and analogues), followed by benzene and substituted derivatives, fatty acyls (largely fatty amides), organooxygen compounds (mostly carbohydrates and carbohydrate conjugates), prenol lipids (mostly diterpenoids, retinoids, and sesquiterpenoids), and flavonoids (mostly flavonoid glycosides and hydroxyflavonoids). A large number of features were also classified as stilbenes, the chemical class represented in the ClassyFire chemical ontology that encompasses the characteristic bibenzyls found in Radula spp. Known compounds from liverworts were tentatively annotated and are listed in Table 1.

Sunburst plot showing an overview on the richness of classified metabolite compounds. Broad compound classes are represented in the center while specific classifications are represented on the exterior. Colours correspond to the assigned classes. Due to readability the names of some classes were removed from the plot. An interactive zoomable plot is available in the supplementary vignettes and on Zenodo

Tentatively annotated liverwort specialized metabolites. Full details are found in the Supplementary Information

CompoundFormulaMolar MassIonizationTentative Feature
Bisabola-1,3,5,7(14),10- pentaeneC15H20200.32PositiveFT0671, FT0672
Ar-tenuifoleneC15H20200.32PositiveFT0671, FT0672
Eudesma-1,4(15)-11- trieneC15H22202.23PositiveFT0692
Myli-4(15)-eneC15H22202.33PositiveFT0692
Cis-calameneneC15H22202.33PositiveFT0692
CupareneC15H22202.33PositiveFT0692
XanthorrizolC15H22O218.33PositiveFT0828 - FT0832
2-cuparenolC15H22O218.33PositiveFT0828 - FT0832
CyclocolorenoneC15H22O218.33PositiveFT0828 - FT0832
β-herbertenolC15H22O218.33PositiveFT0828 - FT0832
Trans-NerolidolC15H26O222.37PositiveFT0861
(E)-farnesolC15H26O222.37PositiveFT0861
3-[2-(3-Methoxyphenyl)ethyl]phenolC15H16O2228.29PositiveFT0923, FT0925
3,4′-DimethoxybibenzylC16H18O2242.31PositiveFT1057, FT1059
1,2-Bis(3-methoxyphenyl)ethaneC16H18O2242.32PositiveFT1057, FT1059
Lunularic acidC15H14O4258.1NegativeFT0814-FT0820
Radulanin AC19H20O2280.37PositiveFT1451, FT1454, FT1458
2,2-Dimethyl-5-hydroxy- 7-(2-phenylethyl)- chromene*C19H20O2280.4PositiveFT1454, FT1458
4-(3-Methyl-2-butenyl)-5-phenethylbenzene-1,3-diolC19H22O2282.38PositiveFT1480, FT1483, FT1484, FT1487
NegativeFT1001, FT1008, FT1009, FT1011
4-PrenyldihydropinosylvinC19H22O2282.38PositiveFT1480, FT1483, FT1484, FT1487
NegativeFT1001, FT1008, FT1009, FT1011
Radulanin A methyl etherC20H22O2294.39PositiveFT1623, FT1624, FT1625, FT1626, FT1627
NegativeFT1111, FT1112
8-[2-(4-Hydroxyphenyl)ethyl]-3-methyl-2,5-dihydro-1-benzoxepin-6-olC19H20O3296.37NegativeFT1132, FT1133, FT1135, FT1136, FT1139, FT1140, FT1141, FT1142, FT1143, FT1144, FT1147
5-Methoxy-2-(3-methylbut-2-en-1-yl)-3-(2-phenylethyl)phenolC20H24O2296.41PositiveFT1658, FT1660
NegativeFT1146, FT1148
4-(3-Methyl-2-Butenyl)-5-(2-Phenylethyl)-3-MethoxyphenolC20H24O2296.41PositiveFT1658, FT1660
NegativeFT1146, FT1148
2-[(3,3-Dimethyloxiran-2-yl)methyl]-5-(2-phenylethyl)benzene-1,3-diolC20H24O2296.41PositiveFT1658, FT1660
NegativeFT1146, FT1148
3-Methoxy-5-(2-phenylethyl)-2-prenylphenolC20H24O2296.41PositiveFT1658, FT1660
NegativeFT1146, FT1148
2-[(3,3-Dimethyloxiran-2-yl)methyl]-5-(2-phenylethyl)benzene-1,3-diolC19H22O3298.38NegativeFT1167, FT1168
Kaempferol 3-methyl-etherC16H12O6300.26NegativeFT1200, FT1201
2,2-Dimethyl-5-hydroxy-7-(2-phenylethyl)-2 H-1-benzopyran-6-carboxylic acidC20H20O4324.38NegativeFT1483, FT1484, FT1485, FT1486, FT1489, FT1491, FT1494, FT1496
Radulanin EC20H20O4324.38NegativeFT1483, FT1484, FT1485, FT1486, FT1489, FT1491, FT1494, FT1496
Radulanin HC20H20O4324.4PositiveFT2017 - FT2020
NegativeFT1484-1486, FT1489-1494, FT1496
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Publication 2023
11-dehydrocorticosterone Amides Amino Acids Benzene Benzopyrans Bibenzyls Carbohydrates Carboxylic Acids derivatives Diterpenes Flavonoids Glycosides Lipids Liverworts Peptides prenol Retinoids Sesquiterpenes Stilbenes

Caterpillars and host plants—The common buckeye (J. coenia) is a specialist nymphalid species that inhabits the southern United States and Mexico with migration into northern states during the summer (Robinson, 2002 ). The larvae primarily feed on plants containing iridoid glycosides (IGs), which is a secondary plant metabolite that acts as a feeding-stimulant, oviposition cue, and sequestered to deter natural enemies (Bowers, 1984 (link); Pereyra and Bowers, 1988 (link); Camara, 1997 (link); Theodoratus and Bowers, 1999 (link)). The plant families containing IGs utilized by the buckeye are Scrophulariaceae, Plantaginaceae, Verbenaceae, and Acanthaceae (Bowers, 1984 (link)).
At our study site in Yuba Gap, California, the native host of the buckeye is the yellow monkeyflower (M. guttatus: Phrymaceae). Mimulus guttatus is a perennial and facultative annual plant that contains phenylpropanoid glycosides (PPGs), one of which, verbascoside, was determined to be a feeding stimulant for buckeyes (Holeski et al., 2013 (link)). Unlike with IGs, Holeski et al. (2013) (link) showed that verbascoside was not sequestered by the larvae feeding on an artificial diet. However, it is unknown whether derivatives of verbascoside are sequestered. The levels of verbascoside in the leaves vary depending on the plant’s life history strategy with annual plants containing higher vebascoside concentrations (Holeski et al., 2013 (link)).
The non-native host plant at this study site is the narrowleaf plantain (P. lanceolata: Plantaginaceae). The buckeye has incorporated P. lanceolata into its host diet breadth after its colonization of North Americas 200 years ago (Thomas et al., 1987 (link)). Plantago lanceolata contains two iridoid glycosides, aucubin and catalpol in relatively equal amounts at concentrations that range between 5%–12% dry weight total IGs (Cavers et al., 1980 (link); Bowers, 1984 (link); Bowers and Collinge, 1992 (link)). Plantago lanceolata has been shown to be a high-quality host plant for the buckeye by positively influencing larval development and growth, which confer fitness consequences during adulthood (Bowers, 1984 (link); Nieminen et al., 2003 (link)).
Both the native and novel host are available to the local buckeye population. We collected adults from this site and reared their offspring in the lab on either the assigned native or novel host to measure performance on each host. The common buckeye (J. coenia) is a specialist nymphalid species that inhabits the southern United States and Mexico with migration into northern states during the summer (Robinson, 2002 ). The larvae primarily feed on plants containing iridoid glycosides (IGs), which is a secondary plant metabolite that acts as a feeding-stimulant, oviposition cue, and sequestered to deter natural enemies (Bowers, 1984 (link); Pereyra and Bowers, 1988 (link); Camara, 1997 (link); Theodoratus and Bowers, 1999 (link)). The plant families containing IGs utilized by the buckeye are Scrophulariaceae, Plantaginaceae, Verbenaceae, and Acanthaceae (Bowers, 1984 (link)).
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Publication 2023
Acanthaceae acteoside Adult Aesculus aucubin catalpol derivatives Diet Glycosides Iridoid Glycosides Larva Life History Strategies Mimulus Oviposition Plantaginaceae Plantago Plantago lanceolata Plants Scrophulariaceae SERPINA3 protein, human Verbenaceae
Animals were euthanized with an overdose of sodium pentobarbital (100 mg/kg, intraperitoneally) after in vivo hemodynamic measurement, and then were rapidly perfused via inferior vena cava with precooled physiological saline for 5 min. The hearts were quickly removed, sectioned, and prepared for paraffin embedding and cryo-section. Routine staining techniques in paraffin embedded tissue (7-µm) included hematoxylin and eosin (H&E) staining (25˚C, 15 min) and Masson's trichrome staining for evaluating interstitial collagen deposition. Tetraethyl rhodamine isothiocyanate-conjugated wheat germ agglutinin (Invitrogen; Thermo Fisher Scientific, Inc.) plus 4,6-diamidino-2-phenylindole (DAPI; 5 mg/ml; Vector Laboratories, Inc.) staining was used to measure cardiomyocyte cross-sectional area, which was examined with a fluorescent microscope (80i; Nikon Corporation) as previously described by the authors (13 (link)). All image analysis was performed in a blinded manner by Image Pro Plus (version 4.5; Media Cybernetics, Inc.).
For detecting lipid droplet, Oil Red O staining was used in cryo-sectioned heart tissue (10 µm), and the sections were fixed with 10% buffered formalin (25˚C, 24 h) and stained with Oil Red O working solution (25˚C, 30 min). Hematoxylin was used for counterstaining. The perirenal adipose tissue was removed and smeared over a slide serving as the positive controls. For detecting sulfated proteoglycans, paraffin-embedded sections were stained with toluidine blue. Alcian blue/periodic acid-Schiff (AB-PAS) was used in paraffin-embedded sections to detect acidic sulfated mucins (AB positive), O-glycosides (PAS positive) and sialic acid (PAS positive) as previously described (14 (link)).
Transmission electron microscopy was performed for evaluating myocardial ultrastructure. Briefly, the samples from LV were fixed in 2.5% glutaraldehyde in 0.1 M phosphate buffer (pH 7.2; 4˚C, 24 h), post-fixed in 1.0% OsO4, dehydrated in alcohol and acetone solution, embedded in Epon812, sectioned with LKB ultramicrotome, and stained with uranyl acetate followed by lead citrate, then observed with a transmission electron microscope.
Publication 2023
Acetone Acids Alcian Blue Animals Buffers Citrates Cloning Vectors Collagen Cryoultramicrotomy DAPI Drug Overdose Eosin Ethanol Formalin Glutaral Glycosides Heart Hematoxylin Hemodynamics Lipid Droplet Microscopy Mucins Myocardium Myocytes, Cardiac N-Acetylneuraminic Acid Paraffin Paraffin Embedding Pentobarbital Sodium Periodic Acid Phosphates physiology Proteoglycan rhodamine isothiocyanate Saline Solution Tissue, Adipose Tissues Tolonium Chloride Transmission Electron Microscopy Ultramicrotomy uranyl acetate Vena Cavas, Inferior Wheat Germ Agglutinins
A murine LIM model was prepared as previously reported40 (link). We created a mouse eyeglass frame that conformed to the contour of the mouse's head and printed it out using a three-dimensional printer. A negative 30 D lens made of PMMA was created for myopia induction. Myopic induction using the − 30 D lens showed greater myopic shift compared to the form-deprivation myopic model40 (link). With some differences from the LIM model used previously, we used binocular myopic induction instead of monocular induction. The left and right eyes of the glasses were adjusted by the shape of the mouse skull frame and fixed on the stick with a screw, and then glued the Stick to the mouse skull with a self-cure dental adhesive system. This was done under general anesthesia with the combination of midazolam (Sandoz K.K., Minato, Japan), medetomidine (Domitor®, Orion Corporation, Turku, Finland), and butorphanol tartrate (Meiji Seika Pharma Co., Ltd., Tokyo, Japan) (MMB). The dosage for each mouse was 0.01 ml/g.
During the myopia induction phase, mice were given either normal (MF, Oriental Yeast Co., Ltd, Tokyo, Japan) or mixed chow containing the candidate chemical 0.0667 percent GBEs (INDENA JAPAN CO., Tokyo, Japan #9,033,008). 0.0667% GBEs contain 24% of the flavonol glycosides of quercetin, kaempferol, and isorhamnetin and 6% terpene trilactones. The corresponding concentration of GBEs mixed chow was 200 mg/kg/day, which is consistent with the concentration of GBEs that causes the significantly high activity of EGR-1 in vitro experiments. The addition of GBEs and the production of 0.0667% GBEs mixed chow are all produced by chow manufacturing company (Oriental Yeast Co., LTD., Tokyo, Japan).
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Publication 2023
3-methylquercetin Asian Persons Butorphanol Tartrate Cranium Dental Health Services EGR1 protein, human Eyeglasses Flavonols General Anesthesia Glycosides Head kaempferol Lens, Crystalline Medetomidine Midazolam Mus Myopia Polymethyl Methacrylate Quercetin Reading Frames Self Cure adhesive Terpenes Yeast, Dried
Dried samples (50 g) of all selected plant parts were extracted in 100% methanol (300 mL) by cold maceration method. All of the extracts were concentrated using a vacuum pump rotatory evaporator from Buchi, New Castle, USA. The concentrated extracts were dried using a freeze dryer from IIShin Lab Co (South Korea) to obtain the lyophilized extract. Phytochemical screening was performed according to the method reported by Bhatnagar et al.38 Qualitative phytochemical screening of major groups of secondary metabolites, such as alkaloids, anthraquinones, flavonoids, glycosides, phenolics, reducing sugars, saponins, tannins, and terpenoids, was performed.
The Folin–Ciocalteu method was used to estimate the TPC in crude extract samples.39 (link) In brief, 1 mL of 2 N Folin–Ciocalteu phenol reagent was mixed with 1 mL of 1 mg/mL plant extract (prepared by dissolving it in methanol), and then the mixture was diluted by the addition of 5 mL distilled water. After incubating for 5 minutes, 1 mL of 10% Na2CO3 solution was added, and then the mixture was incubated for 1 hour in the dark at room temperature. The absorbance of the final mixture was measured at 725 nm using a UV-visible spectrophotometer (Shimadzu, Japan). TPC was expressed as micrograms gallic acid equivalent per milligram of extract (µg GAE/mg), obtained by calibration curves of gallic acid at 500, 400, 300, 200, 100, and 50 µg/mL concentrations.
The aluminum chloride chelation method was used to approximate the amount of flavonoids in all plant extracts.40 (link) First, a stock solution (1 mg/mL) of each plant extract in methanol was diluted with water in a 1:5 ratio and mixed with 0.3 mL of 5% sodium nitrite solution. Then, the mixture was incubated for 5 minutes and 0.3 mL of 10% of AlCl3 was added to it. This was followed by the addition of 2 mL of 1 M sodium hydroxide. The absorbance of the final mixture was taken at 510 nm using a UV-visible spectrophotometer. Total flavonoid was expressed as micrograms of quercetin equivalent per milligram (µg QE/mg) of the plant extract, obtained by calibration curves of quercetin at 500, 400, 300, 200, 100, and 50 µg/mL concentrations.
Publication 2023
Alkaloids Aluminum Chloride Anthraquinones Cold Temperature Complex Extracts Flavonoids folin Freezing Gallic Acid Glycosides Methanol Phenol Phytochemicals Plant Extracts Plants Quercetin Saponins Sodium Hydroxide Sodium Nitrite Sugars Tannins Terpenes Vacuum

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More about "Glycosides"

Glycosides are a diverse class of chemical compounds that are widely found in plants and play crucial roles in various biological processes.
These compounds consist of a sugar molecule (glycone) bonded to a non-sugar molecule (aglycone).
Glycosides are known for their diverse pharmacological properties, including anti-inflammatory, antimicrobial, and cardioactive effects.
Researching glycosides is essential for developing new therapeutic agents and understanding their physiological functions.
The PubCompare.ai platform can streamline glycoside research by helping scientists locate the most effective and reproducible protocols from literature, preprints, and patents, and analyze them using AI-driven tools to optimize their glycoside studies and achieve more reliable results.
Closely related terms and compounds include saponins, phenolic glycosides, cardiac glycosides, and cyanogenic glycosides.
These glycoside subclasses exhibit a range of biological activities and are often studied for their potential therapeutic applications.
Solvents commonly used in glycoside research include DMSO, methanol, and formic acid, which can be used for extraction, purification, and analysis.
Analytical techniques such as HPLC, using an Equity-1 fused silica capillary column and a Milli-Q system for high-quality water, can be employed to identify and quantify glycosides.
Glycosides like chlorogenic acid, caffeic acid, quercetin, and rutin are examples of plant-derived glycosides that have garnered significant interest due to their potential health benefits and therapeutic applications.
Understanding the structure, function, and distribution of these glycosides can provide valuable insights for researchers and developers of glycoside-based products.