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Lung CT COVID-19 batch 1
<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 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="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>
Processed lung Nuclear-Seq data for healthy donors
<p>This repository includes the lung Nuclear-Seq data for healthy donors</p>
CONCORDANCE OF BRONCHOSCOPIC AND RADIOLOGICAL FINDINGS IN SUSPECTED LUNG CANCER AND ITS OUTCOME- A CROSS SECTIONAL STUDY IN A TERTIARY CARE HOSPITAL
<p>Background</p> <p>Bronchoscopy and CT thorax are the most common investigations utilized to screen and diagnose lung cancer. Their individual utility in diagnosing lung cancer has been described affirmatively in existing literature. Studies correlating the airway characteristics of the two modalities in lung cancer are few.</p> <p>Objectives</p> <p>To analyze and characterize lung lesions bronchoscopically and correlate the same radiologically by CT of the thorax and thus assess its positive predictive value.</p> <p>Methods</p> <p>56 consecutive adults who presented to Respiratory Medicine Department at a tertiary care hospital in South India from November 2018 to June 2020, having Clinico-radiological suspicion of malignancy and fulfilling study criteria were recruited. They were subjected to CECT of thorax and bronchoscopy. All bronchoscopic procedures were performed using a protocolized number of passes for biopsy. The baseline demographic and clinical data, findings of bronchoscopy and CT and biopsy reports were recorded. Cohens kappa coefficient was used to estimate the agreement of findings on the two modalities. Statistical analysis was performed using SPSS software.</p> <p>Results</p> <p>Bronchoscopy was normal in 22.14 % of cases, the corresponding CTs also revealed normal airway. Bronchoscopy revealed airway lesions in 78.6 % cases; the corresponding CT revealed airway abnormalities in only 46.41 % of cases, among these 52.1 % of cases revealed an exophytic growth. There was fair strength of agreement for the two modalities in the detection of airway lesions of lung cancer. [k= 0.38, p = <0.001]. CECT thorax has a negative predictive value of 44 %, a sensitivity of 59.1 % and specificity of 100 % at 95 % CI in the detection of airway lesions.</p> <p>Conclusion</p> <p>CT is not definitive in the evaluation of lung cancer, especially in those with central disease. Combination of the two modalities improves the diagnostic outcome in patients with lung cancer.</p> <p>Keywords- Thoracic Oncology, Bronchogenic Carcinoma, Adenocarcinoma of lung, Bronchoscopy, Diagnostic Imaging, Multidetector Computed Tomography.</p>
Single-Cell Transcriptomics Reveals Pre-existing COVID-19 Vulnerability Factors in Lung Cancer Patients
<p>This dataset contains the processed scRNA-seq data and code used to investigate the association between lung cancers and COVID-19. Please refer to the article 'Single-Cell Transcriptomics Reveals Pre-existing COVID-19 Vulnerability Factors in Lung Cancer Patients' for the detailed data and method description.</p> <p>covid_cancer.rds: the processed scRNA-seq data saved as a Seurat object.</p> <p>notebooks.zip: Jupyter notebooks containing code for data analysis.</p>
Fig. 1 in An overview on the role of plant-derived tannins for the treatment of lung cancer
Fig. 1. Molecular targets modulated by tannins in lung cancer. Tannins Abbreviations: CASU - casuarinin; EGCG - epigallocatechin-3-gallate; FPTF - fructus phyllanthi tannin fraction; GA – gallic acid; GERA - geraniin; GRA – granatin A; GRB – granatin B; GSPs - grape seed proanthocyanidins; GSPC - grape seed procyanidins; OEB - oenothein B; PAC – proanthocyanidin rich cranberry fraction; PARE – procyanidin rich extract from sorghum bran; PBOG - prodelphinidin B-2 3′-Ogallate; PCC – procyanidins from cinnamomi cortex; PCCC – procyanidin C1 from cinnamomi cortex; PRFR – proanthocyanidin rich fraction from red rice; TA - tannic acid; TT – total tannins. Abbreviations for molecules: AP-1 - Activator protein 1; Apaf-1 - Apoptotic protease activating factor 1; BAX - BCL2 Associated X; BCL2 - Bcell lymphoma 2; BCL-XL - B-cell lymphoma-extra large; CD31 - cluster of differentiation 31; CDK - cyclin dependent kinase; CDKN1A - cyclin-dependent kinase inhibitor; c-FLIP - FLICE-like inhibitory protein; Cip1/p21 - cyclin-dependent kinase inhibitor 1; COX-2 – cyclooxygenase-2; Cyt C - cytochrome C; E-Cad – E-cadherin; EMT – epithelial-to-mesenchymal transition; Fas - apoptosis antigen 1; FasL - Fas ligand; FN - fibronectin; GR – glutathione reductase; GSH – reduced glutathione; GST - glutathione S-transferase; 15-HETE - 15-hydroxyeicosatetraenoic acid; IGFBP-3 - insulin like factor binding protein 3; IGF-2R - insulin-like growth factor 2 receptor; Kip1/p27 - cyclin-dependent kinase inhibitor 1B; MAPK - mitogen-activated protein kinase; MDM2 - mouse double minute 2 homolog; mFasL - membrane-bound FasL; miR – microRNA; NANOG - transcriptional factor; N-cad - N-cadherin; NOX - NADPH oxidase; NF-kB - nuclear factor kappa-light-chainenhancer of activated B cells; NRF2 - nuclear factor erythroid 2 (NFE2)-related factor 2; NQO1 - NAD(P)H:quinone oxidoreductase; OCT-4 - octamer-binding transcription factor 4; PARP - poly-ADP ribose polymerase; PCNA - proliferating cell nuclear antigen; PGE2 - prostaglandin E2; 6-keto-PGF1α - 6-keto-prostaglandin F1α; PTEN - phosphatase and tensin homolog; PTGIS - prostacyclin synthase; pMDM2 – phospho mouse double minute 2 homolog; pAKT – phosphorylated protein kinase B; pERK1/2 – phosphorylated extracellular signal-regulated kinase 1/2; pJNK1/2 – phosphorylated c-Jun N-terminal kinase 1/2; pNF-kB - phosphorylated nuclear factor kappa-light-chain-enhancer of activated B cells; pPI3K – phosphorylated phosphatidylinositol 3-kinase; α-SMA – alpha-smooth muscle actin; pSmad2 – phosphorylated SMAD family member 2; pSmad3 – phosphorylated SMAD family member 3; p21/WAF1 - cyclin-dependent kinase inhibitor 1; p22phox - human neutrophil cytochrome b light chain; p47phox – neutrophil cytosol factor 1; pp38 – phosphorylated p38; Rb - retinoblastoma protein; sFasL - soluble FasL; Smac/ DIABLO - Second mitochondria-derived activator of caspase/direct inhibitor of apoptosis-binding protein with low pI; SNAIL1 - Zinc finger protein SNAI1; SOX2 - SRY (sex determining region Y)-box 2; pEGFR – phosphorylated epidermal growth factor receptor; TGF-βR1 - transforming growth factor beta receptor; UGT - uridine diphosphate glucuronosyl transferase; VIM - vimentin; VEGF - vascular endothelial growth factor; XIAP - X-linked inhibitor of apoptosis protein; ZO1 - Zonula occludens1. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in An overview on the role of plant-derived tannins for the treatment of lung cancer
Fig. 2. Schematic representation of the mechanism of action of tannins as apoptosis inducers, by targeting multiple cellular signal transduction pathways involved in cancer. Abbreviations: AA - arachidonic acid; Akt - protein kinase B; Axl - AXL receptor tyrosine kinase; BAX - BCL2 Associated X; BCL2 - B-cell lymphoma 2; BCL-XL - B-cell lymphoma-extra large; cAMP - adenosine 3′,5′-cyclic monophosphate; CASU - casuarinin; COX2 – cyclooxygenase 2; Cyt C - cytochrome c; EGCG - epigallocatechin-3-gallate; EGFR - epidermal growth factor receptor; EP receptor - E prostanoid receptor; FADD - Fas-associated protein with death domain; Fas - apoptosis antigen 1; FasL - Fas ligand; FPTF - fructus phyllanthi tannin fraction; GA – gallic acid; GERA - geraniin; GRA - granatin A; GRB - granatin B; GSP - grape seed proanthocyanidins; GSPC - grape seed procyanidins; 15-HETE - 15-Hydroxyeicosatetraenoic acid; IP receptor - prostacyclin receptor; 15-LOX-2 - 15-lipoxygenase-2; MAPK - mitogen-activated protein kinase; MMP - mitochondrial membrane potential; OEB - oenothein B; PAC – proanthocyandin-rich cranberry fraction; PARP - poly-ADP ribose polymerase; PBOG - prodelphinidin B-2 3′-O-gallate; PGE - prostaglandin E2; PGI - prostaglandin I2; PKA - protein kinase A; PI3K - 2 2 phosphatidylinositol 3-kinase; PLA2 - phospholipase A2; PTGIS - prostacyclin synthase; ROS - reactive oxygen species; TA – tannic acid; TAM receptors - receptor tyrosine kinases; Tyro3 - TYRO3 protein tyrosine kinase. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
lung
<p>small RNASeq dataset from GTEX lung data.</p>
Spatiotemporal Profiling Unveiling the Cellular Organization Patterns and Local Protumoral Immune Microenvironment Remodeling in Early Lung Adenocarcinoma Progression
<p>Spatial cellular organization patterns (COPs) in tumor microenvironment influence the tumor progression and therapeutic response, however, little is known about the cellular composition and functional potential of these multicellular structures during lung adenocarcinoma progression. Here, we integrate spatial transcriptomics with single cell RNA sequencing to characterize the local tumor and immunological landscape of samples from 8 patients with lung adenocarcinoma at different pathological stages. We identified ten COPs that show distinct associations with local immune states and clinical outcomes, including survival and therapy response. The local infiltration levels of regulatory and dysfunctional immune cells are increased with pathological progression. Cell-to-cell interactions between malignant cells and tumor microenvironment (TME) cells were involved in protumor immune state remodeling. Finally, we detected a group of malignant cells that were specifically located at the tumor boundary, representing a more aggressive state, were involved in the invasion of invasive adenocarcinoma (IAC). Altogether, these results can improve our understanding of the local microenvironment characteristics that underlie LUAD progression and may facilitate the identification of drug targets to prevent invasive progression and biomarkers for diagnosis.</p><p>If you're going to use this dataset, please cite 'Haojie Chen et al. 2023. Protumoral Immune Microenvironment Remodeling in Early Lung Adenocarcinoma Progression. bioRxiv. https://doi.org/10.1101/2023.10.29.564580'</p>
Endogenous hsa-circ_0007113 binds hsa-miR-515-5p to regulate senescence in human embryonic lung fibroblasts
Open the record for dataset details and reuse information.
Symptom Burden and Unmet Supportive Care Needs in Lung Cancer Patients Undergoing First or Second Line Immunotherapy
ClinicalTrials.gov study NCT03741868. IPD Sharing: NO. Countries: 1. Publications: 2.
Development of a Fluorescent Visualization System for Non-visible Lung Cancer Nodules
ClinicalTrials.gov study NCT06101394. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of Dexmedetomidine on Oxygenation and Lung Mechanics in Morbidly Obese Patients Undergoing Bariatric Operations
ClinicalTrials.gov study NCT02843698. IPD Sharing: NO. Countries: 1. Publications: 1.
Remote Monitoring of Interstitial Lung Disease
ClinicalTrials.gov study NCT04850521. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Elimination of PTV Margins Based on MRI-guided Adaptive Stereotactic Radiotherapy for Non-small Cell Lung Cancer With Brain Metastasis
ClinicalTrials.gov study NCT06582940. IPD Sharing: NO. Countries: 1. Publications: 8.
Pursed Lip Breathing in Interstitial Lung Disease
ClinicalTrials.gov study NCT02934750. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Hamstring Muscle Length on Lunge Distance in Fencing Athletes
ClinicalTrials.gov study NCT06530342. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Tempus Sculptor Study: Small Cell Lung Cancer (SCLC) Observational Study
ClinicalTrials.gov study NCT05257551. IPD Sharing: Not stated. Countries: 1. Publications: 21.
Methylprednisolone After Split-course Chemoradiotherapy For Bulky Local Advanced None-small Cell Lung Cancer
ClinicalTrials.gov study NCT03661567. IPD Sharing: UNDECIDED. Countries: 1. Publications: 16.
Bevacizumab After Chemoradiotherapy For Locally Advanced Lung Adenocarcinoma
ClinicalTrials.gov study NCT03904563. IPD Sharing: NO. Countries: 1. Publications: 13.
Early Specialized Cardiovascular Intervention Based on Impedance Cardiography in Locally Advanced Non-small Cell Lung Cancer Patients
ClinicalTrials.gov study NCT04980716. IPD Sharing: Not stated. Countries: 1. Publications: 25.
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