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Figure 8 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 8 Docking interaction and binding mode of sitagliptin and DPP-IV enzyme. The hydrogen bonds are presented in green dotted lines, the π-π staked interactions are presented in magenta dotted lines and the π-alkyl interactions are presented in pink dotted lines.
Figure 4 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 4 Docking interaction and binding mode of acarbose and alpha-amylase enzyme. The hydrogen bonds are presented in green dotted lines.
Figure 7 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 7 Docking interaction and binding mode of palmatine and alpha-glucosidase enzyme. The hydrogen bonds are presented in green dotted lines and the π-alkyl interactions are presented in pink dotted lines.
Figure 2 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 2 Alpha-glucosidase enzyme inhibting activity ((IC50(µM) of palmatine, glimepiride, metformin and standard drug(acarbose). The result are shown as Mean ± standard deviation in triplicates (n = 3). The data were statistically analyzed by One-way ANOVA followed by Dunnet's post hoc test. Graph showed that acarbose(a) vs. glimepiride = p < 0.0001 = d; acarbose vs. metformin = p < 0.0001 = d; acarbose vs. palmatine = p < 0.0001 = d. Glimepiride vs. metformin = No significant = d, glimepiride vs. palmatine = No significant = d and metformin vs. palmatine = No significant = d.
Figure 1 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 1 Alpha-amylase enzyme inhibting activity (IC50(µM) of palmatine, glimepiride, metformin and standard drug(acarbose). The result are shown as Mean ± standard deviation in triplicates (n = 3). The data were statistically analyzed by One-way ANOVA followed by Dunnet's post hoc test. Graph showed that acarbose(a) vs. glimepiride = p < 0.001 = c; acarbose vs. metformin = p < 0.01 = b; acarbose vs. palmatine = p < 0.0001 = d. Glimepiride vs. Metformin = p < 0.001 = c, glimepiride vs. palmatine = p < 0.0001 = d, metformin vs. palmatine = p < 0.0001 = d.
Figure 9 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 9 Docking interaction and binding mode of palmatine and DPP-IV enzyme. The hydrogen bonds are presented in green dotted lines, the π-π staked interactions are presented in magenta dotted lines and the π-alkyl interactions are presented in pink dotted lines.
Figure 3 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 3 DPP-IV enzyme inhibting activity ((IC50(µM) of glimepiride, metformin, palmatine and standard drug(sitagliptin). The result are shown as Mean ± standard deviation in triplicates (n = 3). The data were statistically analyzed by One-way ANOVA followed by Dunnet's post hoc test. Graph showed that sitagliptin (a) vs. palmatine = p < 0.0001 = d. Glimepiride and metformin = NA = No activity.
Figure 6 from: Okechukwu P, Sharma M, Tan WH, Chan HK, Chirara K, Gaurav A, Al-Nema M (2020) In-vitro antidiabetic activity and in-silico studies of the binding energies of palmatine and the standard compounds with the three receptors of alpha amylase, alpha glucosidase, and DPP-IV enzyme. Pharmacia 67(4): 363-371. https://doi.org/10.3897/pharmacia.67.e58392
Figure 6 Docking interaction and binding mode of acarbose and alpha-glucosidase enzyme. The hydrogen bonds are presented in green dotted lines and the π-alkyl interactions are presented in pink dotted lines.
Supplementary material for "Compounds with antiviral, anti-inflammatory and anticancer activity identified in wine via high resolution mass spectrometry and bioinformatics analyses"
<p>Wine contains a variety of molecules with potential beneficial effects on human health. Our aim was to examine the wine components with high-resolution mass spectrometry including high-resolution tandem mass spectrometry in two wine types made from grapes with or without the fungus <em>Botrytis cinerea</em>, or “noble rot.” For LC-MS/MS analysis, 12 wine samples (7 without and 5 with noble rotting) from 4 different wineries were used and wine components were identified and quantified. Results: 288 molecules were identified in the wines and the amount of 169 molecules was statistically significantly different between the two wine types. A database search was carried out to find the molecules, which were examined in functional studies so far, with high emphasis on molecules with antiviral, anti-inflammatory and anticancer activities. Conclusions: A comprehensive functional dataset related to identified wine components is also provided highlighting the importance of components with potential health benefits.</p>
Methoxylated aromatic compounds modulate the transport activity of Methermicoccus shengliensis MATE family transporter
<p>RNAseq raw data</p>
Figure 1 from: Fadhilah QG, Santoso I, Maryanto AE, Abdullah S, Yasman Y (2021) Evaluation of the antifungal activity of marine actinomycetes isolates against the phytopathogenic fungi Colletotrichum siamense KA: A preliminary study for new antifungal compound discovery. Pharmacia 68(4): 837-843. https://doi.org/10.3897/pharmacia.68.e72817
Figure 1 Results of the antibiosis assay of SM14 isolate in PDA filtrate medium. A. control, B. 6 days, C. 9 days, D. 12 days.
Figure 3 from: Fadhilah QG, Santoso I, Maryanto AE, Abdullah S, Yasman Y (2021) Evaluation of the antifungal activity of marine actinomycetes isolates against the phytopathogenic fungi Colletotrichum siamense KA: A preliminary study for new antifungal compound discovery. Pharmacia 68(4): 837-843. https://doi.org/10.3897/pharmacia.68.e72817
Figure 3 Phylogenetic analysis of marine actinomycetes isolates. The neighbor-joining tree of the three marine actinomycetes isolates (SM11, SM14, and SM15) was based on the 16S rRNA gene sequences. Streptomyces albus subsp. albus DSM 40313T is represented as an outgroup. The bootstrap values, based on 1,000 replications, are shown at the nodes; only values above 50% are given. The scale bar indicates 0.0050 substitutions per nucleotide position.
Figure 4 in Quality of cosmetics with active caffeine in cream and gel galenic bases prepared by compounding pharmacies
Figure 4. Viscosity curve for cream-based cosmetics, at time 0 and stored for 30, 60 and 90 days. Of which: C1 (cream-based cosmetic at Pharmacy 1), C2 (cream-based cosmetic at Pharmacy 2), C3 (cream-based cosmetic at Pharmacy 3) and C4 (cream-based cosmetic at Pharmacy 4).
Figure 3 in Quality of cosmetics with active caffeine in cream and gel galenic bases prepared by compounding pharmacies
Figure 3. Flow curve for cream-based cosmetics at time 0 and stored for 30, 60 and 90 days.Of which:C1 (cream-based cosmetic at Pharmacy 1), C2 cream-based cosmetic at Pharmacy 2), C3 (cream-based cosmetic at Pharmacy 3) and C4 cream-based cosmetic at Pharmacy 4).
Fig. 4 in In-vitro antioxidative potential of different fractions from Prunus dulcis seeds: Vis a vis antiproliferative and antibacterial activities of active compounds
Fig. 4. IC50 values of DPPH scavenging activity of isolated compounds and that of standard.
Fig. 3 in In-vitro antioxidative potential of different fractions from Prunus dulcis seeds: Vis a vis antiproliferative and antibacterial activities of active compounds
Fig. 3. Structure of isolated compounds from ethyl acetate fraction.
Fig. 2. Key HMBC correlations for compounds 1 and 2 in Insight into tetrahydrofuran lignans from Isatis indigotica fortune with neuroprotective and acetylcholinesterase inhibitor activity
Fig. 2. Key HMBC correlations for compounds 1 and 2.
Fig. 5. Experimental and calculated ECD spectra for compounds 1a in Insight into tetrahydrofuran lignans from Isatis indigotica fortune with neuroprotective and acetylcholinesterase inhibitor activity
Fig. 5. Experimental and calculated ECD spectra for compounds 1a/1b and 2 in MeOH.
Fig. 5. X in Diverse undescribed compounds from the rhizome of Zingiber officinale Rosc. And their anti-inflammatory activity
Fig. 5. X-ray crystallographic analysis of 3.
Fig. 3 in Diverse undescribed compounds from the rhizome of Zingiber officinale Rosc. And their anti-inflammatory activity
Fig. 3. Experimental and calculated ECD curves of compound 1.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.