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16,682 results for “Lung.”
Optimization of lung tissue pre-treatment by bead homogenization for subsequent culturomics
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Disease trajectories in hospitalized COVID-19 patients are predicted by clinical and peripheral blood signatures representing distinct lung pathologies
<p><span>COVID-19 is characterized by a broad range of symptoms and disease trajectories. Understanding the correlation between clinical biomarkers and lung pathology over the course of acute COVID-19 is necessary to understand its diverse pathogenesis and inform more precise and effective treatments. Here, we present an integrated analysis of longitudinal clinical parameters, peripheral blood biomarkers, and lung pathology in COVID-19 patients from the Brazilian Amazon. We identified core clinical and peripheral blood signatures differentiating disease progression between recovered patients from severe disease and fatal cases. Signatures were heterogenous among fatal cases yet clustered into two patient groups: “early death” (< 15 days of disease until death) and “late death” (> 15 days). Progression to early death was characterized systemically and in lung histopathology by rapid, intense endothelial and myeloid activation/chemoattraction and presence of thrombi, associated with SARS-CoV-2<sup>+</sup> macrophages. In contrast, progression to late death was associated with fibrosis, apoptosis and abundant SARS-CoV-2<sup>+</sup> epithelial cells in post-mortem lung, with cytotoxicity, interferon and Th17 signatures only detectable in the peripheral blood 2 weeks into hospitalization. Progression to recovery was associated with higher lymphocyte counts, Th2 and anti-inflammatory-mediated responses. By integrating ante-mortem longitudinal systemic and spatial single-cell lung signatures, we defined an enhanced set of prognostic clinical parameters predicting disease outcome for guiding more precise and optimal treatments.</span><span> Finally, this study represents a major advance in the investigation of acute respiratory infections by integrating serial clinical data and peripheral blood samples with histopathological and </span><span>spatially-resolved single-cell </span><span>analyses of post-mortem lung samples.</span></p>
Integrating Metagenomic and Metabolomic Insights into Host-Microbe Connections for Gut-Lung Axis in Childhood Asthma
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A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation (DICOM VERSION)
<p>We constructed a groundbreaking lung CT dataset, which includes 330 annotated nodules from 95 patients. It is worth noting that we have integrated the results of patient clinical diagnosis, frozen diagnosis, and pathological diagnosis, supplementing this with labeled lung cancer types on 308 samples containing nodules.</p>
Blood memory CD8 T cell phenotypes in lung cancer patients predict immune checkpoint treatment responses
<p>Rscript for figure generation and data analysis:</p> <p>GenerateFigures.R</p> <p> </p> <p>Seurat objects containing processed data after quality control:</p> <p><a href="../api/records/10867209/draft/files/NCCS_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">NCCS_For_Zenodo.RDS</a> - NCCS discovery cohort.</p> <p><a href="../api/records/10867209/draft/files/Pavia_For_Zenodo.RDS/content" target="_blank" rel="noopener noreferrer">Pavia_For_Zenodo.RDS</a> - Pavia validation cohort.</p> <p> </p> <p>RDS files containing DEGs or differentially abundant surface markers:</p> <p>TestResults2Groups.rds - Cell type specific LTR vs Non Responder DEG </p> <p>TestResults2GroupsADT.rds - Cell type specific LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnly.rds - Cell type specific LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyADT.rds - Cell type specific LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3Groups.rds - Cell type specific LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p>TestResults2GroupsGeneralRNA.rds - Across cell type LTR vs Non Responder DEG </p> <p>TestResults2GroupsGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers</p> <p>TestResults2GroupsLungOnlyGeneralRNA.rds - Across cell type LTR vs Non Responder DEG on lung samples only</p> <p>TestResults2GroupsLungOnlyGeneralADT.rds - Across cell type LTR vs Non Responder differential surface markers on lung samples only</p> <p>TestResults3GroupsGeneralRNA.rds - Across cell type LTR vs R vs Non Responder differential DEG</p> <p>TestResults3GroupsGeneralADT.rds - Across cell type LTR vs R vs Non Responder differential surface markers</p> <p> </p> <p>Logistic regression models trained on the NCCS discovery cohort:</p> <p>PerCellPredictions <CellType> * - Celltype specific models predicting either LTR, R or control group trained on all NCCS samples</p> <p>PerCellPredictions_2Groups_LungOnly <CellType> * - Celltype specific models predicting either LTR or NonResponder group, trained on lung samples only.</p> <p>PerCellPredictions_2Groups_<CellType> * - Celltype specific models predicting either LTR or NonResponder trained on all NCCS samples</p>
CT dataset for "Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer" Part 2
<p>To develop and validate a radiomics-based method for lung cancer detection, chest CT images of patients with pulmonary nodules from clinical 5 centers were collected. We hope this large-scale dataset could facilitate both clinical research for automatic lung cancer detection and diagnoses, and engineering research for 3D detection, segmentation and classification. Due to size limit of zenodo.org, we split the whole CT images sets into 2 parts; This is the Part 2.</p> <p>This dataset is a research effort of thousands of hours by experienced thoracic surgeons, and radiologists. We kindly ask you to respect our effort by appropriate citation and keeping data license.</p>
Data and code for bovine lung ultrasound studies
<p>Data and code repository for a study published in a traditional journal</p>
Inhibition of Lung Microbiota-derived Proapoptotic Peptides Ameliorate Acute Exacerbation of Pulmonary Fibrosis
<p>The files show the microarray data obtained during epitope mapping of the newly developed monoclonal antibodies against corisin, the complete protein sequences of newly isolated <em>Staphylococcus haemolyticus</em> strains (1b, 7b and 12b), and the raw data of all figures and tables.</p>
Treatment with 3-aminobenzamide during ex vivo lung perfusion of damaged rat lungs reduces graft injury and dysfunction after transplantation
<p>Ex vivo lung perfusion (EVLP) with pharmacological reconditioning may increase donor lung utilization for transplantation (LTx). 3-Aminobenzamide (3-AB), an inhibitor of poly (ADP-ribose) polymerase (PARP), reduces ex vivo lung injury in rat lungs damaged by warm ischemia (WI). Here we determined the effects of 3-AB reconditioning on graft outcome after LTx. Three groups of donor lungs were studied: Control (Ctrl): 1 hour WI + 3 hours cold ischemia (CI) + LTx; EVLP: 1 hour WI + 3 hours EVLP + LTx; EVLP + 3-AB: 1 hour WI + 3 hours EVLP + 3-AB (1 mg<sup>.</sup>mL<sup>−1</sup>) + LTx. Two hours after LTx, we determined lung graft compliance, edema, histology, neutrophil counts in bronchoalveolar lavage (BAL), mRNA levels of adhesion molecules within the graft, as well as concentrations of interleukin-6 and 10 (IL-6, IL-10) in BAL and plasma. 3-AB reconditioning during EVLP improved compliance and reduced lung edema, neutrophil infiltration, and the expression of adhesion molecules within the transplanted lungs. 3-AB also attenuated the IL-6/IL-10 ratio in BAL and plasma, supporting an improved balance between pro- and anti-inflammatory mediators. Thus, 3-AB reconditioning during EVLP of rat lung grafts damaged by WI markedly reduces inflammation, edema, and physiological deterioration after LTx, supporting the use of PARP inhibitors for the rehabilitation of damaged lungs during EVLP.</p>
Effects of cold or warm ischemia and ex-vivo lung perfusion on the release of damage associated molecular patterns and inflammatory cytokines in experimental lung transplantation
<p>Lung transplantation (LTx) is associated with sterile inflammation, possibly related to the release of damage associated molecular patterns (DAMPs) by injured allograft cells. We have measured cellular damage and the release of DAMPs and cytokines in an experimental model of LTx after cold or warm ischemia and examined the effect of pretreatment with ex-vivo lung perfusion (EVLP).</p>
Quantitative Evaluation of COVID-19 Pneumonia Lung Extension by Specific Software and Correlation with Patient Clinical Outcome
<p>Quantitative Evaluation of COVID-19 Pneumonia Lung Extension by Specific Software and Correlation with Patient Clinical Outcome</p> <p>Dataset regarding lung extension of covid 19 </p>
Files for Manuscript "A pulmonologist's guide to perform and analyse cross-species single-lung-cell transcriptomics"
<p>Input Files for Manuscript "A pulmonologist’s guide to perform and analyse cross-species single-lung-cell transcriptomic"</p> <p>See https://github.com/GenStatLeipzig/pulmonologists_interspecies_scRNA for details.</p> <p>Manuscript authored by:</p> <p>Peter Pennitz1,2*, Holger Kirsten3*, Vincent D. Friedrich3,4, Emanuel Wyler5, Cengiz Goekeri1,2,6, Benedikt Obermayer7, Gitta A. Heinz8, Mir-Farzin Mashreghi8,9, Maren Büttner10,11 Jakob Trimpert12, Markus Landthaler5,13, Norbert Suttorp2, Andreas C. Hocke1,2, Stefan Hippenstiel2, Mario Tönnies14, Markus Scholz3, Wolfgang M. Kuebler15,16, Martin Witzenrath1,2,16, Katja Hoenzke1,2 and Geraldine Nouailles1,2,# </p> <p> </p> <p>1 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Division of Pulmonary Inflammation, Berlin, Germany. </p> <p>2 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany. </p> <p>3 University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany. </p> <p>4 Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany. </p> <p>5 Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany. </p> <p>6 Cyprus International University, Faculty of Medicine, Nicosia, Cyprus. </p> <p>7 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Unit Bioinformatics, Berlin, Germany. </p> <p>8 Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), A Leibniz Institute, Therapeutic Gene Regulation, Berlin, Germany. </p> <p>9 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Berlin, Germany. </p> <p>10 University of Bonn, Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, Bonn, Germany. </p> <p>11 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Systems Medicine, Bonn, Germany. </p> <p>12 Freie Universität Berlin, Institute of Virology, Berlin, Germany. </p> <p>13 Humboldt-Universität zu Berlin, Institute for Biology, IRI Life Sciences, Berlin, Germany. </p> <p>14 HELIOS Clinic Emil von Behring, Department of Pneumology and Department of Thoracic Surgery, Chest Hospital Heckeshorn, Berlin, Germany. </p> <p>15 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Physiology, Berlin, Germany. </p> <ol> <li> <p>German Center for Lung Research (DZL), Berlin, Germany. </p> </li> </ol> <p> </p> <p>* Authors contributed equally to this work </p> <p> </p> <p>References for original datasets :</p> <p>Human Charité: Hocke A, Hönzke K, Obermayer B, Baumgardt M, Wyler E, Hippenstiel S, Mache C. Charité Berlin /Berlin Institute of Health. GEO accessions GSM5958267, GSM5958272, GSM5958283, GSM5958285</p> <p>Human Travaglini et al.: published at <a href="https://www.synapse.org/">https://www.synapse.org</a> by Travaglini et al. (<a href="https://doi.org/10.1038/s41586-020-2922-4">https://doi.org/10.1038/s41586-020-2922-4</a>)</p> <p>Monkey: published at <a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a> by Speranza et al. (<a href="https://doi.org/10.1126/scitranslmed.abe8146">https://doi.org/10.1126/scitranslmed.abe8146</a>)</p> <p>Hamster: Charité Berlin, see: <a href="https://doi.org/10.1038/s41467-021-25030-7">https://doi.org/10.1038/s41467-021-25030-7</a></p> <p>Mouse: Pennitz P, Witzenrath, M, Nouailles G, Berlin Charité.</p> <p>Rat and Pig: published at <a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a> by Raredon et al. (<a href="https://doi.org/10.1126/sciadv.aaw3851">https://doi.org/10.1126/sciadv.aaw3851</a>) .</p> <p>Annotation: Ensembl BioMart</p>
Identification of biomarkers for the earlier detection of lung cancer: a systematic review and meta-analysis
<p>All data analysed during this study are included in this excel sheet showing the steps in this systematic literature review process.</p>
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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A Riskscore Model for Predicting Survival, Tumor Microenvironment, Immunotherapy and drug sensitivity of Lung Squamous Cell Carcinoma Based on PI3K/AKT/MTOR Pathway-Related Genes
<p>Firstly, the data we provide is the raw data downloaded from the TCGA database. Secondly, we provide the following explanations for the raw data, taking Figure 1 as an example:</p> <p>First, we selected a LUSC dataset from the TCGA database that includes RNA-seq data (FPKM values) and clear clinical information, consisting of 51 normal samples and 501 LUSC samples. The raw data <span>can be found in</span> the "mRNA" document in the "Fig.1" file<span>, and t</span>he data includes TCGA <span>id</span> information.</p> <p>Next, the data was imported into "mRNA_edgeR" to analyze whether the RNA-seq data from these 552 samples meet the criteria of |logFC| > 0.585 and FDR < 0.05, <span>and the results are shown in</span> the "diffSig" table. Subsequently, the genes in the "diffSig" table were intersected with 105 PAGs, <span>and</span> 44 key PAGs <span>were identified, </span>as shown in Figure 1.</p> <p>The <span>other</span> figures can <span>also </span>be reproduced sequentially based on the methods described <span>above</span>.</p> <p>Furthermore, due to the limited number of LUSC patients, the clinical information in the database is relatively incomplete. Hence, the clinical information that we provide constitutes the entire content available in the database.</p>
Single cell data from Imaging Mass Cytometry of mouse lung tumours treated with KRAS-G12C and immune checkpoint inhibitors (Dataset 3)
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Characterizing NSD3 Amplification in Lung Cancer
<p><strong>SGC Open Notebook Project to Characterize the HMTase NSD3</strong></p> <p><strong>Exp020 Objective: </strong>NSD3 (WHSC1L1) is amplified in ~5% of Non-Small Cell Lung Cancer patients( cBioPortal : Cerami et<br> al. Cancer Discov. 2012 and Gao et al. Sci. Signal. 2013). However, the implications of this event on the<br> formation and progression of the disease are unclear. While NSD3 may be a driver of lung cancer, it is also<br> plausible that this locus is simply amplified at a higher frequency in the context of cancer-associated genomic<br> instability. To dive deeper into this question I will use The Cancer Genome Atlas (TCGA) lung cancer<br> data-sets to look for associations between NSD3 amplification and mutational status as well as gene expression<br> profiles. This data has been generated by the TCGA Research Network: http://cancergenome.nih.gov/. I<br> hypothesize that if NSD3 amplification is a driving force in a subset of lung tumors, these samples will share<br> similar gene expression profiles and exhibit higher expression levels of NSD3. Here, I am using FirebrowserR<br> (Deng M., et al. Database. 2017 - PMID:28062517), an R client for Broad Institute’s Firehose Web API,<br> which allows TCGA data processed by the Firehose Pipeline to be directly imported into R for analysis.</p>
NSD3-Short Promotes Migration of A549 Lung Cancer Cells
<p><strong>SGC Open Notebook Project to Characterize the HMTase NSD3</strong></p> <p><strong>Exp025 Objective:</strong> In a previous experiment (exp024), we observed an increase in E-cadherin expression in response to knockdown of the short isoform of NSD3. To determine if this change is functionally relevant, we performed wound healing assays to measure any corresponding alteration in the migratory potential of A549 lung epithelial cancer cells.</p>
NSD3-Short Represses E-cadherin Expression in A549 Lung Epithelial Cancer Cells
<p><strong>SGC Open Notebook Project to Characterize the HMTase NSD3</strong></p> <p><strong>Exp024 Objective: </strong>In a previous experiment (exp023), we show that siRNA-mediated knockdown of NSD3 increases expression of E-cadherin, a marker of epithelial cell identity. To determine which isoform of NSD3 is involved in promoting epithelial to mesenchymal transition, I have designed siRNA that targets either the long or short isoform and again use E-cadherin expression as a marker. Additionally, I have started using A549 lung epithelial cancer cells (https://www.atcc.org/Products/All/CCL-185) as my primary model. This cell line proliferates faster and is more amenable to <em>in vitro</em> experimentation than the H1299 cell line I was using previously.</p>
Data related to manuscript: Comparison of size distribution and electrical particle sensor measurement methods for particle lung deposited surface area (LDSAal) in ambient measurements with varying conditions (accepted for publication in Aerosol Research)
<p>These files include data which was utilised to calculate the results in the manuscript (https://doi.org/10.5194/ar-2024-13). </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.