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Figure 2 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501
Figure 2 A. Visualization of interactions between Neurotensin 8–13 and receptors (4GRV) and B. Visualization of molecular docking between the receptor 4GRV and natural ligand.
Figure 1 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501
Figure 1 A. Neurotensin receptor (4GRV) and B. Overlay of the docked pose of Neurotensin 8–13 with the co-crystallized ligand 4GRV.
Figure 4 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501
Figure 4 RMSD (a) and RMSF (b) value of docosahexaenoic acid (blue), eicosapentaenoic acid (maroon), and heneicosapentaenoic acid (green).
Figure 5 from: Musfiroh I, Megawati G, Diah Herawati DM, Nama Putra O, Sylvia Nurrasjid E (2023) Molecular dynamic of omega-3 compounds as an anti-obesity agent into GPR-120 receptor. Pharmacia 70(4): 1541-1548. https://doi.org/10.3897/pharmacia.70.e115501
Figure 5 SASA plot of docosahexaenoic acid (blue), eicosapentaenoic acid (maroon), and heneicosapentaenoic acid (green).
Figure 4 from: Llorens L, Ferriol P, Tomàs J, García MT, Gil L (2023) Can floral volatile organic compounds contribute to the taxonomy of the Rhamnus sect. Alaternus? Italian Botanist 16: 149-164. https://doi.org/10.3897/italianbotanist.16.116255
Figure 4 Non-metric multidimensional scaling ordination (NMDS) of floral scent profiles for Rhamnus alaternus (violet and empty circles), Rhamnus × bermejoi (blue and half black circles) and Rhamnus ludovici-salvatoris (green and black points).
Figure 3 from: Llorens L, Ferriol P, Tomàs J, García MT, Gil L (2023) Can floral volatile organic compounds contribute to the taxonomy of the Rhamnus sect. Alaternus? Italian Botanist 16: 149-164. https://doi.org/10.3897/italianbotanist.16.116255
Figure 3 VOCs similarity histogram of Rhamnus alaternus (violet), Rh. × bermejoi (blue) and Rh. ludovici-salvatoris (green). Colour intensity is related to the level of presence of a component in each sample.
Figure 2 from: Llorens L, Ferriol P, Tomàs J, García MT, Gil L (2023) Can floral volatile organic compounds contribute to the taxonomy of the Rhamnus sect. Alaternus? Italian Botanist 16: 149-164. https://doi.org/10.3897/italianbotanist.16.116255
Figure 2 Location of the Rhamnus populations included in the study. (See Suppl. material 1 for population abbreviations). Rh. alaternus open squares; Rh. ludovici-salvatoris filled triangles; Hybrids filled circle.
Figure 1 from: Llorens L, Ferriol P, Tomàs J, García MT, Gil L (2023) Can floral volatile organic compounds contribute to the taxonomy of the Rhamnus sect. Alaternus? Italian Botanist 16: 149-164. https://doi.org/10.3897/italianbotanist.16.116255
Figure 1 Inflorescences, flowers and major pollinator (Apis mellifera) of RhamnusA–CRh. alaternusD, ERh. ludovici-salvatorisFRh. × bermejoiG leaves of the three species.
Supplementary material 2 from: Llorens L, Ferriol P, Tomàs J, García MT, Gil L (2023) Can floral volatile organic compounds contribute to the taxonomy of the Rhamnus sect. Alaternus? Italian Botanist 16: 149-164. https://doi.org/10.3897/italianbotanist.16.116255
Floral scent chemistry
Supplementary material 1 from: Llorens L, Ferriol P, Tomàs J, García MT, Gil L (2023) Can floral volatile organic compounds contribute to the taxonomy of the Rhamnus sect. Alaternus? Italian Botanist 16: 149-164. https://doi.org/10.3897/italianbotanist.16.116255
Geographical location and sexuality of Rhamnus sampled plants
Figure 3 from: Nguyen N-VT, Nguyen CV, Duong NT, Dai X-TT, Nguyen KT, Le C-TT (2024) Optimization of ultrasound-assisted extraction using response surface methodology and quantification of polyphenol compounds in Avicennia officinalis L. from Vietnam. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e115528
Figure 3 3D response surface curve showing the influences of independent variables on the TFC (A–F).
Figure 2 from: Nguyen N-VT, Nguyen CV, Duong NT, Dai X-TT, Nguyen KT, Le C-TT (2024) Optimization of ultrasound-assisted extraction using response surface methodology and quantification of polyphenol compounds in Avicennia officinalis L. from Vietnam. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e115528
Figure 2 3D response surface curve showing the influences of independent variables on the TPC (A–F).
Figure 4 from: Nguyen N-VT, Nguyen CV, Duong NT, Dai X-TT, Nguyen KT, Le C-TT (2024) Optimization of ultrasound-assisted extraction using response surface methodology and quantification of polyphenol compounds in Avicennia officinalis L. from Vietnam. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e115528
Figure 4 HPLC chromatogram of A. officinalis leaves extract. a Mobile phase solvent; b Solute sovent; c Extraction solvent; d Spiked sample; e Mixed standards solution; f Standard addition. (C1: Chlorogenic acid, C2: p-coumaric acid, C3: Sinapic acid, C4: Taxifolin, C5: Luteolin -7-O-glucoside, C6: Cinnamic acid).
Figure 1 from: Nguyen N-VT, Nguyen CV, Duong NT, Dai X-TT, Nguyen KT, Le C-TT (2024) Optimization of ultrasound-assisted extraction using response surface methodology and quantification of polyphenol compounds in Avicennia officinalis L. from Vietnam. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e115528
Figure 1 Influence of methanol concentration, ratio of solid to liquid, temperature and time ultrasonic on the extration of TPC and TFC from A. officinalis leave extracts.
Figure 1 from: Talib WH, Atawneh S, Shakhatreh AN, Shakhatreh GN, Rasheed aljarrah IS, Hamed RA, Adel banyyounes D, Al-Yasari IH (2024) Anticancer potential of garlic bioactive constituents: Allicin, Z-ajoene, and organosulfur compounds. Pharmacia 71: 1-23. https://doi.org/10.3897/pharmacia.71.e114556
Figure 1 Enhancement of DNA repair mechanisms by garlic compounds (created by BioRender.com). This diagram illustrates how garlic compounds enhance DNA repair mechanisms: DNA Damage: The process begins with DNA damage, represented as breaks and mutations in the DNA strands. Recognition: The damaged DNA is recognized by cellular repair machinery, signaling the need for repair. Garlic Compounds: Garlic compounds, highlighted in the diagram, play a crucial role at this stage. They are known for their Genoprotective properties. DNA Repair Mechanism: The diagram illustrates the sequential steps of DNA repair, including excision, polymerization, and ligation. Enhanced Repair: Garlic compounds facilitate and enhance the DNA repair process, as indicated by connecting lines and arrows.
Figure 2 from: Talib WH, Atawneh S, Shakhatreh AN, Shakhatreh GN, Rasheed aljarrah IS, Hamed RA, Adel banyyounes D, Al-Yasari IH (2024) Anticancer potential of garlic bioactive constituents: Allicin, Z-ajoene, and organosulfur compounds. Pharmacia 71: 1-23. https://doi.org/10.3897/pharmacia.71.e114556
Figure 2 Angiogenesis processes and garlic compound interference (created by: BioRender.com). Fig. 2 provides an overview of the intricate process of angiogenesis within the tumor microenvironment and demonstrates how garlic compounds interfere with this critical pathway. Tumor Microenvironment: On the left, the tumor microenvironment is depicted as the origin of angiogenesis.
Unveiling CNS Cell Morphology with Deep Learning: A Gateway to Anti-Inflammatory Compound Screening
<p>Understanding the connection between cellular structure and observable traits is vital for comprehending cell behavior, especially in neuropathological conditions. Current limitations exist in applying advanced image analysis, like deep learning, to central nervous system cells due to challenges such as the need for extensive labeled data, difficulty in detecting subtle changes, and the presence of batch effects. The study focuses on overcoming these issues in the context of neuroinflammation, utilizing a deep learning approach to effectively analyze morphological changes in neuronal and microglial cells. This innovative method aids in studying neuroinflammation, facilitating the screening of potential therapeutic compounds and bridging gaps in neuropathological research and pharmaceutical development.</p>
A compound-target pairs dataset: differences between drugs, clinical candidates and other bioactive compounds
<p>Please find newer versions of the dataset on the <a href="https://ftp.ebi.ac.uk/pub/databases/chembl/Drug_Target_dataset/" target="_blank" rel="noopener">ChEMBL FTP site.</a></p> <p> </p>
Figure 3 from: Youssef AMM, Maaty DAM, Al-Saraireh YM (2024) Qualitative chemical compounds analysis and in vitro estimation of antiproliferative, antidiabetic and anti-Alzheimer's disease effects of Ononis natrix (L.) family Fabaceae. Pharmacia 71: 1-11. https://doi.org/10.3897/pharmacia.71.e119755
Figure 3 The antiproliferative actions of O. natrix and cisplatin against cancerous cell lines colorectal adenocarcinoma (CaCo-2), breast cancer (MCF-7), prostate cancer (PC-3), hepatocellular carcinoma (HepG-2), and cervical cancer (HeLa), and normal human fetal lung fibroblast (WI-38). nsP = 0.5 and ***p = 0.0001 demonstrate significant differences related to cisplatin. The t-test was applied to compare the O. natrix and cisplatin.
Figure 7 from: Youssef AMM, Maaty DAM, Al-Saraireh YM (2024) Qualitative chemical compounds analysis and in vitro estimation of antiproliferative, antidiabetic and anti-Alzheimer's disease effects of Ononis natrix (L.) family Fabaceae. Pharmacia 71: 1-11. https://doi.org/10.3897/pharmacia.71.e119755
Figure 7 Determination of IC50 values of O. natrix and acarbose for inhibition of Butyrylcholinesterase enzyme.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.