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Figure 7 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 7 Anti-proliferation activity of several concentrations of TQ with 106.63 µM silymarin against EMT-6/CPR cell lines.
Figure 13 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 13 Shows that the percentage of change in tumor volume in EMT-6/CPR cell line was significant (P-value < 0.05) in all treatment groups except the silymarin group. This graph was obtained using GraphPad prism.
Figure 18 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 18 Effect of TQ (25 mg/kg), silymarin (50 mg/kg), their combinations, cisplatin (0.7 mg/kg), and control group on serum AST level measured by (IU/L).
Figure 12 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 12 A plot of change in average tumor size (mm³) vs. time in (days) of treatment in EMT-6/P cell line.
Figure 6 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 6 Anti-proliferation activity of various concentrations of silymarin with 29.58 µM TQ against EMT-6/P cell lines.
Figure 5 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 5 Anti-proliferation activity of various concentrations of TQ with 142.40 µM silymarin against EMT-6/P cell lines.
Figure 17 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 17 Showed that there is no significant (ns) difference in serum ALT between the healthy group and other treatment groups. This graph was obtained by GraphPad Prism.
Figure 2 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 2 Anti-proliferation effect of TQ in single treatment against EMT-6/P and EMT-6/CPR cell lines.
Figure 20 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 20 Effect of TQ (25 mg/kg), silymarin (50 mg/kg), their combinations, cisplatin (0.7 mg/kg), and control group on serum creatinine level measured by (mg/dl).
Figure 19 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 19 Showed that there is no significant difference in serum AST between the healthy group and other treatment groups. This graph was obtained by GraphPad Prism.
Figure 11 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 11 Shows that the percentage of change in tumor volume in EMT-6/P cell line was significant (P-value < 0.05) in all treatment groups. This graph was obtained using GraphPad prism
Figure 3 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 3 Anti-proliferation effect of silymarin in single treatment against EMT-6/P and EMT-6/CPR cell lines.
Figure 8 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997
Figure 8 Anti-proliferation activity of several concentrations of silymarin with 66 µM TQ against EMT-6/CPR cell lines.
Use of 3D-iUS in combination with AR for resection of intradural spine tumors
<p>Use of 3D-iUS in combination with AR for resection of intradural spine tumors</p>
Figure 5 from: Salamah N, Cantika CD, Nurani LH, Guntarti A (2024) Authentication of citrus peel oils from different species and commercial products using FTIR Spectroscopy combined with chemometrics. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e118789
Figure 5 PCA score plot of the sweet orange, lime, lemon peel oils, turpentine oil and commercial oil products A, B and C.
Figure 3 from: Salamah N, Cantika CD, Nurani LH, Guntarti A (2024) Authentication of citrus peel oils from different species and commercial products using FTIR Spectroscopy combined with chemometrics. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e118789
Figure 3 ATR-FTIR spectra of three commercial oil products in the 3500–500 cm-1 region. (1: Product A, 2: Product B, 3: Product C).
Figure 4 from: Salamah N, Cantika CD, Nurani LH, Guntarti A (2024) Authentication of citrus peel oils from different species and commercial products using FTIR Spectroscopy combined with chemometrics. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e118789
Figure 4 Correlation curves between the actual values (x-axis) and the predicted values (y-axis): (A) the model's calibration using the 1650–1450 cm-1 region (optimised wavenumbers), (B) internal validation and (C) external validation.
Figure 2 from: Salamah N, Cantika CD, Nurani LH, Guntarti A (2024) Authentication of citrus peel oils from different species and commercial products using FTIR Spectroscopy combined with chemometrics. Pharmacia 71: 1-7. https://doi.org/10.3897/pharmacia.71.e118789
Figure 2 ATR-FTIR spectra of sweet orange peel oil (MKJM) mixed with turpentine oil (MT) at different concentrations.
Identification of species by combining molecular and morphological data using convolutional neural networks
<p>Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy (<i>></i>98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity.</p>
Data from: Cultural transmission of tool use combined with habitat specializations leads to fine-scale genetic structure in bottlenose dolphins
[No abstract entered]
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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.