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16,682 results for “Lung.”
Figure 5 from: Abbas AH, Mahmood AAR, Tahtamouni LH, Al-Mazaydeh ZA, Rammaha MS, Alsoubani F, Al-bayati RI (2021) A novel derivative of picolinic acid induces endoplasmic reticulum stress-mediated apoptosis in human non-small cell lung cancer cells: synthesis, docking study, and anticancer activity. Pharmacia 68(3): 679-692. https://doi.org/10.3897/pharmacia.68.e70654
Figure 5 Compound 5 induces the ER-mediated apoptosis. A) Representative Western blot showing the enhanced release of Smac/DIABLO but not cytochrome c from the mitochondria (MF) into the cytosol (CF) of compound 5-treated A549 lung cancer cells (GI50). Equal protein loading was controlled by staining membranes with Ponceau S (a representative section of the stained membrane is shown). The experiment was repeated three times and the corresponding quantification is shown in (B), B) Quantification of smac/DIABLO and cytochrome C levels in vehicle-treated control A549 cells and cells treated with GI50 amount of compound 5 or 5% DMSO as a negative control. M: mitochondria fraction; C: cytosol fraction. Scale bars: mean ± SEM of three independent experiments. ** p < 0.01 compared to vehicle-treated control cells, C) Representative Western blot showing the induction of phosphorylation of eukaryotic initiation factor-2 (eIF-2) in A549 cells treated with GI50 amounts of compound 5. For comparison purposes, thapsigargin (TG), an ER stress-causing drug was used (3 μM, 2 h) as a positive control. The experiment was repeated three times.
Figure 4 from: Abbas AH, Mahmood AAR, Tahtamouni LH, Al-Mazaydeh ZA, Rammaha MS, Alsoubani F, Al-bayati RI (2021) A novel derivative of picolinic acid induces endoplasmic reticulum stress-mediated apoptosis in human non-small cell lung cancer cells: synthesis, docking study, and anticancer activity. Pharmacia 68(3): 679-692. https://doi.org/10.3897/pharmacia.68.e70654
Figure 4 Compound 5 causes caspase activation. A) qRT-PCR analysis of caspase 3, 4, 8 and 9 mRNA in compound 5-treated cells (GI50) as compared to vehicle-treated control cells [set as 1 arbitrary unit (a.u.)]. Values were normalized to β-actin. Scale bars: mean ± SEM of three independent experiments performed in triplicates. *p < 0.05, **p < 0.01 compared to vehicle-treated control cells, B) Representative Western blots showing cleavage "activation' of procaspase 3 to the active form p17, procaspase 4 to the active form p20, and procaspase 9 to the active forms p35/p37. C: vehicle-treated control cells; 0.5%: 0.5% DMSO-treated cells; 1%: 1% DMSO-treated cells; 5%: 5% DMSO-treated cells; C.5: Compound 5-treated cells. The experiment was repeated three times.
Figure 2 from: Abbas AH, Mahmood AAR, Tahtamouni LH, Al-Mazaydeh ZA, Rammaha MS, Alsoubani F, Al-bayati RI (2021) A novel derivative of picolinic acid induces endoplasmic reticulum stress-mediated apoptosis in human non-small cell lung cancer cells: synthesis, docking study, and anticancer activity. Pharmacia 68(3): 679-692. https://doi.org/10.3897/pharmacia.68.e70654
Figure 2 The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide) assay results for compound 5 against MCF10A cells, and white blood cells (WBC) after 24, 48 and 72 h of treatment.
Figure 3 from: Abbas AH, Mahmood AAR, Tahtamouni LH, Al-Mazaydeh ZA, Rammaha MS, Alsoubani F, Al-bayati RI (2021) A novel derivative of picolinic acid induces endoplasmic reticulum stress-mediated apoptosis in human non-small cell lung cancer cells: synthesis, docking study, and anticancer activity. Pharmacia 68(3): 679-692. https://doi.org/10.3897/pharmacia.68.e70654
Figure 3 Compound 5 induces apoptosis in A549 lung cancer cells. A) Fluorescence images of A549 lung cancer cells showing fragmented nuclei after DAPI staining. Scale bar: 10 µm, B) Detection of DNA fragmentation by agarose gel electrophoresis. Cells were treated and genomic DNA was extracted and electrophoresed on 1.5% agarose gels. M: DNA molecular weight marker; C: vehicle-treated control cells; 1%: 1% DMSO-treated cells; 5%: 5% DMSO-treated cells; C.5: Compound 5-treated cells. Arrows indicate DNA fragments. Three experiments were performed with similar results.
Figure 6 from: Abbas AH, Mahmood AAR, Tahtamouni LH, Al-Mazaydeh ZA, Rammaha MS, Alsoubani F, Al-bayati RI (2021) A novel derivative of picolinic acid induces endoplasmic reticulum stress-mediated apoptosis in human non-small cell lung cancer cells: synthesis, docking study, and anticancer activity. Pharmacia 68(3): 679-692. https://doi.org/10.3897/pharmacia.68.e70654
Figure 6 3D View of the target compounds (5, 7A, 6B and 6C) docked against ATP binding site of EGFR kinase.
Multiplexed histology of COVID-19 post-mortem lung samples - CHRONIC CASE 2 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CHRONIC CASE 2 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Ex vivo tissue perturbations coupled to single cell RNA-seq reveal multi-lineage cell circuit dynamics in human lung fibrogenesis
<p>Processed count tables of scRNA-seq data generated for this study.</p>
Multiplexed histology of COVID-19 post-mortem lung samples - CONROL CASE 3 FOV2
<p><strong>Image-based data set of a post-mortem lung sample from a COVID-19 donor (CONTROL CASE 3 FOV2)</strong></p> <p>Each image shows the same field of view (FOV), sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins, intracellular proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have been normalized and intensities adjusted.</p>
Cellular and molecular heterogeneities and signatures, and pathological trajectories of fatal COVID-19 lungs defined by spatial single-cell transcriptome analysis
<p>Spatial in-situ data analysis.</p>
Lung CT COVID-19 batch 6
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 4:<a href="http:// https://zenodo.org/record/8043089"> https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p>
Lung CT COVID-19 batch 4
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Lung CT COVID-19 batch 2
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 4: <a href="https://zenodo.org/record/8043089">https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Lung CT COVID-19 batch 3
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 4: <a href="https://zenodo.org/record/8043089">https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
TFAP2A upregulates SKA3 to promote glycolysis and reduce the sensitivity of lung adenocarcinoma cells to cisplatin
<p>Supplementary data for the article</p>
Lung-MAP: Durvalumab as Second-Line Therapy in Treating Patients With Recurrent Stage IV Squamous Cell Lung Cancer and No Matching Biomarkers
ClinicalTrials.gov study NCT02766335. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Work of Breathing and Mechanical Ventilation in Acute Lung Injury
ClinicalTrials.gov study NCT00961168. IPD Sharing: Not stated. Countries: 0. Publications: 14.
A Study Comparing Two Different Chemotherapy Types in Chinese Patients With Advanced Non Small Cell Lung Cancer
ClinicalTrials.gov study NCT01005680. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Neoadjuvant or Adjuvant Chemotherapy in Patients With Operable Non-small-cell Lung Cancer
ClinicalTrials.gov study NCT00913705. IPD Sharing: Not stated. Countries: 0. Publications: 1.
PRospective Evaluation of Interstitial Lung DIsease Progression With Quantitative CT
ClinicalTrials.gov study NCT05609201. IPD Sharing: YES. Countries: 1. Publications: 0.
Study Evaluating the Addition of Fulvestrant to Erlotinib in Stage IIIB/IV Non-Small Cell Lung Cancer
ClinicalTrials.gov study NCT00592007. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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