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6,222 results for “small cells”
Molecular dynamics dataset for pharmacological repositioning in the treatment of non-small-cell lung cancer
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Data from: Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps - a feasibility study
Objective: The aim of this study was to prospectively evaluate the feasibility of monitoring treatment response to chemotherapy in patients with non-small cell lung carcinoma using functional diffusion maps (fDMs). Materials and Methods: This study was approved by the Cantonal Research Ethics Committee and informed written consent was obtained from all patients. Nine patients (mean age = 66 years; range = 53–76 years, 5 females, 4 males) with overall 13 lesions were included. Imaging was performed within two weeks before initiation of chemotherapy and at one, two, and six weeks after initiation of chemotherapy. Imaging included a respiratory-triggered diffusion-weighted sequence including three b-factors (100, 600, and 800 s/mm2). Treatment response was defined by change in tumor diameter on computed tomography (CT) after two cycles of chemotherapy. Changes in the apparent diffusion coefficient (ADC) on a per-lesion basis and the percentages of voxel with significantly increased or decreased ADCs on fDMs were analyzed using repeated measures analysis of variance (ANOVA). Changes in tumor size were used as covariate to examine the ability of ADCs and fDM parameters to predict treatment response. Results: Repeated measures ANOVA revealed that the percentage of voxels with increased ADCs on fDMs (p = 0.002) as well as the mean ADC increase (p = 0.011) were significantly higher in good responders with a large reduction in tumor size on CT. Conclusion: Our results indicate that the percentage of voxels with significantly increased ADCs on fDMs seems to be a promising biomarker for early prediction of treatment response in patients with non-small cell lung carcinoma. Contrary to averaged values, this approach allows the spatial heterogeneity of treatment response to be resolved.
Supplementary PyMOL sessions for "Small protein blockers of human IL-6 receptor alpha inhibit proliferation and migration of cancer cells"
<p>PyMOL sessions with summary of NEF variants docking to IL-6R. Supplementary to "Small protein blockers of human IL-6 receptor alpha inhibit proliferation and migration of cancer cells".</p>
Supplementary material 8 from: Olszyński RM, Zakrzewski PK, Rimet F, Sulkowska J, Peszek Ł, Żelazna-Wieczorek J (2024) Morphology and phylogeny of Nitzschia nandorii sp. nov. (Bacillariophyceae), a new small-celled lanceolate species from a post-mining reservoir. PhytoKeys 241: 1-26. https://doi.org/10.3897/phytokeys.241.117406
Confocal Laser Scanning Microscopy projection of rotating chloroplast of Nitzschia nandorii sp. nov.
Iterative cell optimization in refinement of small molecule electron diffraction data.
<p>Electron diffraction data for the MOF Vie-1 and for Oseltamivir. Associated with the manuscript "Iterative cell optimization in refinement of small molecule electron diffraction data." in submission process.</p>
FIGURE 47–67 in A new small-celled diatom from Brazil - Luticola minutissima sp. nov., with comparison to the type of the Antarctic L. neglecta Zidarova, Levkov & Van de Vijver
FIGURE 47–67. Luticola neglecta Zidarova, Levkov & Van de Vijver from type material (sample D37 South Shetland Islands: Deception Island, Antarctica). 47–60 Size diminution series, LM. 61, 62 Valve exterior. 63, 64 Detailed external view showing stigma, raphe endings and thread-like depressions in central area. 65 Internal view of valve. 66, 67 Detailed close-ups of internal stigma opening and areoles covered by hymenes. Scale bars = 10 μm (Figs 47–60); 5 μm (Fig. 62); 4 μm (Figs 61, 65); 3 μm (Fig. 63); 1 μm (Figs 66, 67).
FIGURE 38–46 in A new small-celled diatom from Brazil - Luticola minutissima sp. nov., with comparison to the type of the Antarctic L. neglecta Zidarova, Levkov & Van de Vijver
FIGURE 38–46. Detailed SEM close-ups of Luticola minutissima sp. nov. 38, 42 External detail of proximal raphe endings and stigma. 39–41 External detail of areolae and raphe branches with distal endings. 43–45 Valve face, internally. 46 Internal view of stigma, proximal raphe endings and areolae covered by hymenes. Scale bars = 2 μm (Figs 38–42); 5 μm (Figs 43–45); 1 μm (Fig. 46).
FIGURE 1–37 in A new small-celled diatom from Brazil - Luticola minutissima sp. nov., with comparison to the type of the Antarctic L. neglecta Zidarova, Levkov & Van de Vijver
FIGURE 1–37. Luticola minutissima sp. nov. Fig. 19. holotype specimen, LM. 1–28 Valves in size diminution series, LM. 29–37 Valve exterior, SEM. Scale bars = 10 μm (Figs 1–28); 5 μm (Figs 29–31, 35-37); 4 μm (Figs 32–34).
Proteomic and Metabolomic Profiling of Plasma Predict Immune-related Adverse Events in Older Patients with Advanced Non-small Cell Lung Cancer
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FIGURES 17–29. Sellaphora pseudoventralis comb. nov. 17–27. Light microscopy. 29–29. SEM. 28. External valve view. 29 in Sellaphora smirnovii (Bacillariophyta, Sellaphoraceae), a new small-celled species from Lake Glubokoe, European Russia, together with transfer of Navicula pseudoventralis to the genus Sellaphora
FIGURES 17–29. Sellaphora pseudoventralis comb. nov. 17–27. Light microscopy. 29–29. SEM. 28. External valve view. 29. Internal valve view. Scale bars: 10 μm (Figs 17–27), 2 μm (Figs 28–29).
FIGURES 1–16 in Sellaphora smirnovii (Bacillariophyta, Sellaphoraceae), a new small-celled species from Lake Glubokoe, European Russia, together with transfer of Navicula pseudoventralis to the genus Sellaphora
FIGURES 1–16. Sellaphora smirnovii sp. nov. 1–13. Light microscopy, Fig. 7 illustrates the holotype specimen. 14–16. SEM, external valve view. Scale bars: 10 μm (Figs 1–13), 1 μm (Figs 14–16).
Immunotherapy-mediated thyroid dysfunction: genetic risk and impact on outcomes with PD-1 blockade in non-small cell lung cancer
<p>Polygenic risk score weights derived using LDpred for hypothyroidism and thyroid medication use.</p>
FIGURES 21–25 in Actinellopsis murphyi gen. et spec. nov.: A new small celled freshwater diatom (Bacillariophyta, Eunotiales) from Zambia
FIGURES 21–25. SEM images of valve exterior of Actinellopsis murphyi gen. et spec. nov. Figs 21–23. SEM images showing the external valve view. Fig. 24. SEM image of external girdle view showing epi and hypovalves with longer (L) and shorter (S) raphe slits External rimoportulae (R) opening indicated. Fig. 25. SEM image of the external girdle view of dorsal margin. Scale bar = 5 µm (21, 22, 23), 2 µm (24, 25).
FIGURES 1–20 in Actinellopsis murphyi gen. et spec. nov.: A new small celled freshwater diatom (Bacillariophyta, Eunotiales) from Zambia
FIGURES 1–20. LM images of Actinellopsis murphyi gen. et. spec. nov., valves from holotype D-NWU 12-349, Ntumbachushi Falls on Ngona River, in Zambia, showing the size range in valve view. Figs 1–16. Valve view. Figs 17–20. Girdle view. Scale bar = 10 µm.
FIGURES 36–41 in Actinellopsis murphyi gen. et spec. nov.: A new small celled freshwater diatom (Bacillariophyta, Eunotiales) from Zambia
FIGURES 36–41. SEM images of valve interior of Actinellopsis murphyi gen. et. spec. nov. Figs 36–38. SEM images showing the internal valve view. Figs 39–41 SEM images of internal pole showing rimoportulae, helictoglossa and internal areolar openings. Scale bar = 10 µm (37), 5 µm (36, 38), 2 µm (40, 41), 1 µm (39).
FIGURES 26–35 in Actinellopsis murphyi gen. et spec. nov.: A new small celled freshwater diatom (Bacillariophyta, Eunotiales) from Zambia
FIGURES 26–35. SEM images of external head and foot poles of Actinellopsis murphyi gen. et. spec. nov. Figs 26–30. SEM images of external view of head pole. Figs 31–35. SEM images of external view of foot pole. Rimoportula opening (R). Scale bar = 3 µm (26, 27), 2 µm (28, 29, 30, 31, 32, 33, 34, 35).
Fabrication of W-band TWT for 5G small cells backhaul
<p>Underlying data corresponding to the paper: F. André, S. Kohler, V. Krozer, Q.T. Le, R. Letizia, C. Paoloni, A. Sabaawi, G. Ulisse, R. Zimmerman, "Fabrication of W-band TWT for 5G small cells backhaul", 18th International Vacuum Electronics Conference (IVEC 2017), London, United Kingdom April 2017.</p>
Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade
<p>Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligand (PD-L1) blockade. We aimed to use deep-learning algorithm to estimate clinical benefit in patients with non-small-cell lung cancer (NSCLC) before immunotherapy. Peripheral blood samples or tumor tissues of 915 patients from three independent centers were profiled by whole-exome sequencing or next-generation sequencing. Based on convolutional neural network (CNN) and three conventional machine learning (cML) methods, we used multi-panels to train the models for predicting the durable clinical benefit (DCB) and combined them to develop a nomogram model for predicting prognosis. In the three cohorts, the CNN achieved the highest area under the curve of predicting DCB among cML, PD-L1 expression, and tumor mutational burden (area under the curve [AUC] = 0.965, 95% confidence interval [CI]: 0.949–0.978, <em>P</em> < 0.001; AUC =0.965, 95% CI: 0.940–0.989, <em>P</em> < 0.001; AUC = 0.959, 95% CI: 0.942–0.976, <em>P</em> < 0.001, respectively). Patients with CNN-high had longer progression-free survival (PFS) and overall survival (OS) than patients with CNN-low in the three cohorts. Subgroup analysis confirmed the efficient predictive ability of CNN. Combining three cML methods (CNN, SVM, and RF) yielded a robust comprehensive nomogram for predicting PFS and OS in the three cohorts (each <em>P</em> < 0.001). The proposed deep-learning method based on mutational genes revealed the potential value of clinical benefit prediction in patients with NSCLC and provides novel insights for combined machine learning in PD-1/PD-L1 blockade.</p>
Single cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis
<p>Codes and single cell data for "<strong>Single cell atlas of the human neonatal small intestine affected by necrotizing enterocolitis</strong>" paper.</p> <p><strong>File: Tunel_Lyve1_Data.xlsx </strong>– quantifications of Tunel+Lyve1+ cells for n=4 NECs and n=4 neonatal samples.</p> <p><strong>Folder: Cell_ranger_output</strong></p> <p>Data produced using 10x sequencing.</p> <p>This folder contains all the Cell Ranger output raw files from all 11 subjects used for single cell RNA sequencing (5x Neonatal- S1056, S1127, S1212, S1214, S1082, 6x NECs- S1021, S1074, S1095, S1109, S1155, S1193).</p> <p>The neonatal subjects had two 10X runs (except subject 1082) - one done on CD45 negatively selected small intestinal cells and the other on non-selected small intestinal cells.</p> <p>The NEC subjects had one run each (subject 1074 was sequenced twice) with no selection due to low yield in the selected runs.</p> <p><strong>Folder: Seurat_pipeline</strong></p> <p>Done with Seurat package version 3.2.2</p> <p>This folder contains R scripts for Seurat analysis and script for background (BG) subtraction of the data.</p> <ol> <li>BG_subtraction_before_seurat.R - Script that creates a BG subtracted matrix for each CellRanger output before Seurat analysis.</li> <li>seurat_pipline_all_cells_NEC.R - Script for Seurat analysis for the full atlas.</li> <li>Final_seurat_obj_after_filtration_all_cells.rds – rds file of final Seurat object of all cells after pipeline filtration</li> <li>Files description in “RDS_files_for_seurat_pipeline” folder: <ol> <li>list_UMIs_after_BG_subtruction.rds – rds file containing a list of all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script.</li> <li>list_metadata_after_BG_subtruction.rds – rds file containing a list of metadata for all 10x runs used in the study after background subtraction. Used as input file for “seurat_pipline_all_cells_NEC.R” script.</li> </ol> </li> </ol> <p><strong>Folder: Deconvolution_analysis</strong></p> <p>Done on Matlab R2019b</p> <p>This folder contains the script for deconvolution analysis and its input files.</p> <ol> <li>Deconvolution_parsing_NEC_for_zenodo.m - Matlab script to create box plots of deconvolution results and their q- values.</li> <li>Files description: <ol> <li>deconvolution_original8_clusters.mat – structure of deconvolution results. Signature cells were split into the 8 major cell type groups shown in figure 1.</li> <li>cellanneal_split_clust_results.mat – structure of deconvolution results. Signature cells were split into sub groups for each cell type group.</li> </ol> </li> </ol> <p><strong>Folder: Ligand-receptor_interaction_analysis</strong></p> <p>This folder contains matlab and R scripts for ligand-receptor interaction analysis.</p> <ol> <li>run_permutations_for_lig_rec_ratio_tensor_Zenodo.m – main script to run ligand-receptor analysis.</li> <li>calculate_ratio_ligand_receptor.m – function used by the main script.</li> <li>Interactions_visualizations_heatmaps.R – R script for heatmap visualization.</li> <li>Files description in “Mat_files_for_analysis” folder: <ol> <li>all_cells_split_for_interactions.mat – single cell structure of all cells. Cells are split into subgroups for ligand receptor analysis.</li> <li>ramilowsky.mat – ligand-receptor lists taken from Ramilowski et al. 2015 to use in main script and function.</li> <li>tensor_real_q100permutations.mat – output mat file of the interactions ratio, q values and ligand-receptor names for heatmaps visualization in R (input file for “Interactions_visualizations_heatmaps.R”).</li> </ol> </li> </ol>
Supplementary data to "Small-scale variation prevails in the cell shape patterning of green microalgae belonging to the genus Micrasterias (Zygnematophyceae, Viridiplantae)"
<p>The supplementary data consist of 24 TPS files including the landmark coordinates of 12 Micrasterias datasets (two separate digitisations for each dataset). In addition, the R script used for the analyses described in the paper submitted to "Evolutionary Biology" and the utility file with factors for Procrustes ANOVA are also included.</p> <p> </p>
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.