Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"

Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing. The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab. This work is available in medRxiv and has been submitted

opengpl-3.0-or-laterSep 2024View details →

Personalized in silico model for radiation-induced pulmonary fibrosis | (source code, simulation input+output data)

This repository concerns the supplementary material data of the research article entitled "Personalised in silico model for radiation-induced pulmonary fibrosis" that is published in the Royal Society Interface journal (rsif.royalsocietypublishing.org). More specifically, the repository contains the source code of the radiation-induced pulmonary fibrosis simulator, the results produced from the medical image analysis of this study (CT scans and RT dosage maps) for each patient case, the input files necessary to run the simulator and the corresponding output produced respectively. Each patient ID corresponds to each case documented in the research article.

opencc-by-4.0May 2023View details →

LungVis1.0: Active learning AI-powered 3D imaging ecosystem for spatial profiling of lung geometry and pulmonary nanoparticle delivery

The imaging dataset was obtained by light sheet fluorescence microscopy on tissue cleared murine lungs. It includes whole lung autofluorence image, particle fluorescence image, and artifical intelligence nnU-Net generated lung airway segments. The dataset provides 78 healthy murine lung strucutre and airway geometry for C57BL/6 mice and offers comprehensive delivery features including qualitative and quantitative analysis on the temporal and spatial inter- and intra-acinar deposition patterns and NP regional dosimetry for four commonly-used routes of pulmonary delivery,namely intranasal liquid aspiration, intratracheal liquid instillation, ventilator-assisted and nose-only aerosol inhalation. Raw LSFM imaging data collection was carried out between 2017-2021,  the AI code and generated airway segmention were performed in 2021-2022, the whole datasets were then compiled in 2023.  Please ensure to cite our paper for any reuse or reanalysis. Yang, L., Liu, Q., Kumar, P. et al. LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages. Nat Commun 15, 10138 (2024). https://doi.org/10.1038/s41467-024-54267-1 For any inquiries, please feel free to contact us at lin.yang@helmholtz-munich.de

opencc-by-4.0Aug 2023View details →

TBValid collection: Pulmonary tuberculosis validation collection

The TBValid dataset comprises 870 digital patients with different profiles, each with fixed Age, BMI and MtbSputum. Each is identified by a vector of features involving biological and pathophysiological parameters to roughly represent different profiles in the population and initial bacterial load. Individual patient data collected during a clinical trial have been transformed into aggregated data, which are already irreversibly anonymised. Subsequently, these aggregated data have been sampled through the procedure described in “Generation of digital patients for the simulation of tuberculosis with UISS-TB”, doi: 10.1186/s12859-020-03776-z. The obtained derivative dataset, owned by its creators, does not constitute sensitive data according to European laws, and it is impossible with this dataset to re-establish the identity of the patients enrolled in the original clinical trial.

opencc-by-4.0Dec 2022View details →

Dataset for Detection and Segmentation of the Radiographic Features of Pulmonary Edema

Objectives: This comprehensive dataset is well suited for training, evaluating, and using machine learning models to detect, segment, and analyze radiological features associated with pulmonary edema in chest X-ray images. Description: This dataset consists of a collection of chest X-rays extracted from the <a href="https://physionet.org/content/mimic-cxr-jpg/2.0.0/" target="_blank" rel="noopener">MIMIC database</a>, carefully collected at the Beth Israel Deaconess Medical Center. In total, it comprises 1000 chest X-rays obtained from 741 patients with features suggestive of edema. These X-rays were carefully selected for manual annotation. The annotations are rich and detailed, covering specific radiological features commonly associated with pulmonary edema, including cephalization, Kerley lines, pleural effusions, bat wings, and infiltrates. The dataset includes a wide variety of radiological features, with a total of 4263 annotations (Table 1). Furthermore, each chest radiograph is thoughtfully assigned a severity category, categorizing it as "no edema", "vascular congestion", "interstitial edema", or "alveolar edema". Annotation Method: The annotation process was meticulously performed by a highly qualified clinician with over 10 years of radiology experience, utilizing both frontal and lateral views for each chest X-ray study. Cephalization and Kerley lines were delineated using polylines, while other features were delineated using binary masks. This methodological approach was carefully chosen to provide a comprehensive data set that would ensure accuracy in subsequent analyses and label assignments.  Notably, all features are represented as bounding boxes, meticulously defined by their respective upper-left (x1; y1) and lower-right (x2; y2) corners. In addition, selected features are provided with masks encoded in base 64 format. To facilitate seamless decoding, we provide a conversion script called "mask_converter.py" that allows the transformation of encoded masks into a versatile numpy array format. This feature improves the usability of the dataset for precise analysis and deep learning applications. Datasets: <ol> <li>SLY dataset: The dataset contains chest X-ray images labeled by clinicians, including both stacked frontal and lateral images. We obtained this dataset by annotating it on the <a href="https://supervisely.com/" target="_blank" rel="noopener">Supervisely platform</a>, and it is stored in JSON and PNG formats.</li> <li>Source dataset: The dataset is a transformed version of the SLY dataset. In this dataset, all annotations are consolidated into a single spreadsheet, and only frontal view images are represented.</li> <li>Processed dataset: The dataset focuses exclusively on the lung area for analysis, as other areas surrounding the lung typically contain extraneous information that clinicians do not use in their decision-making process.</li> <li>COCO dataset: A collection of subsets prepared in the <a href="https://towardsdatascience.com/how-to-work-with-object-detection-datasets-in-coco-format-9bf4fb5848a4" target="_blank" rel="noopener">COCO format</a> and suitable for training and testing. It includes subsets for each feature and for all features evaluated in this study.</li> </ol> <div> Access to the Study: Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories: <ul> <li>Source code: <a href="https://github.com/ViacheslavDanilov/edema_quantification" target="_blank" rel="noopener">https://github.com/ViacheslavDanilov/edema_quantification</a></li> <li>Dataset: <a href="https://doi.org/10.5281/zenodo.8383776" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8383776</a></li> <li>Lung segmentation models: <a href="https://doi.org/10.5281/zenodo.8393555" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8393555</a></li> <li>Radiographic feature detection models: <a href="https://doi.org/10.5281/zenodo.8393565" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8393565</a></li> </ul>   Table 1. Summary of annotated radiological features and severity labels <table> <tbody> <tr> <td> Radiological feature </td> <td> Number of objects </td> <td> Severity label </td> <td> Number of cases </td> </tr> <tr> <td> Cephalization </td> <td> 1656 </td> <td> No edema </td> <td> 21 </td> </tr> <tr> <td> Kerley line </td> <td> 609 </td> <td> Vascular congestion </td> <td> 74 </td> </tr> <tr> <td> Pleural effusion </td> <td> 317 </td> <td> Interstitial edema </td> <td> 51 </td> </tr> <tr> <td> Bat wing </td> <td> 1604 </td> <td> Alveolar edema </td> <td> 595 </td> </tr> <tr> <td> Infiltrate </td> <td> 77 </td> <td>   </td> <td>   </td> </tr> <tr> <td> TOTAL </td> <td> 4263 </td> <td> TOTAL </td> <td> 741 </td> </tr> </tbody> </table>   </div>

opencc-by-4.0Jan 2021View details →

Supplementary material: Prophylactic antibiotics for adults with chronic obstructive pulmonary disease: a network meta-analysis

Supplementary material for Cochrane review: Janjua S , Mathioudakis AG , Fortescue R , Walker RAE , Sharif S , Threapleton CJD , Dias S . Prophylactic antibiotics for adults with chronic obstructive pulmonary disease: a network meta-analysis. Cochrane Database of Systematic Reviews 2021, Issue 1. Art. No.: <a href="https://archie.cochrane.org/sections/documents/CD013198">CD013198</a>. DOI: <a href="https://archie.cochrane.org/sections/documents/10.1002/14651858.CD013198.pub2">10.1002/14651858.CD013198.pub2</a>.

opencc-by-4.0Jan 2021View details →

Experiment data in support of "Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease"

This archive contains a ZIP archive, `data.zip`, that itself contains the data used in the final sections of the paper. The remainder of the paper's supporting files are available at <a href="https://github.com/daffidwilde/copd-paper/">github.com/daffidwilde/copd-paper/</a> The ZIP archive is structured as follows: <ul> <li>There is a directory, `wasserstein`, for the parameter sweep described in the model construction section of the paper. Its contents are: (i) a file, `main.csv`, describing each parameter and their maximal Wasserstein distance to the observed data, and (ii) three directories, `best`, `median` and `worst`, each containing the simulated queuing results (in `main.csv`) from that sweep with the best, median and worst found parameter sets, respectively (in `params.txt`).</li> <li>The remaining three directories correspond to the experiments conducted in the final section of the paper. Each directory contains two files: (i) `system_times.csv` which holds trial parameters and system time records for every patient to pass through the model in that experiment, and (ii) `utilisations.csv` which holds trial parameters and utilisations for each server in the model for that experiment.</li> </ul>

dryad40/100

Data from: Repeated sensitization of mice with microfilariae of Litomosoides sigmodontis induces pulmonary eosinophilia in an IL-33-dependent manner

Background: Eosinophilia is a hallmark of helminth infections and eosinophils are essential in the protective immune responses against helminths. Nevertheless, the distinct role of eosinophils during parasitic filarial infection, allergy and autoimmune disease-driven pathology is still not sufficiently understood. In this study, we established a mouse model for microfilariae-induced eosinophilic lung disease (ELD), a manifestation caused by eosinophil hyper-responsiveness within the lung. Methods: Wild-type (WT) BALB/c mice were sensitized with dead microfilariae (MF) of the rodent filarial nematode Litomosoides sigmodontis three times at weekly intervals and subsequently challenged with viable MF to induce ELD. The resulting immune response was compared to non-sensitized WT mice as well as sensitized eosinophil-deficient dblGATA mice using flow cytometry, lung histology and ELISA. Additionally, the impact of IL-33 signaling on ELD development was investigated using the IL-33 antagonist HpARI2. Results: ELD-induced WT mice displayed an increased type 2 immune response in the lung with increased frequencies of eosinophils, alternatively activated macrophages and group 2 innate lymphoid cells, as well as higher peripheral blood IgE, IL-5 and IL-33 levels in comparison to mice challenged only with viable MF or PBS. ELD mice had an increased MF retention in lung tissue, which was in line with an enhanced MF clearance from peripheral blood. Using eosinophil-deficient dblGATA mice we demonstrate that eosinophils are essentially involved in driving the type 2 immune response and retention of MF in the lung of ELD mice. Furthermore, we demonstrate that IL-33 drives eosinophil activation in vitro and inhibition of IL-33 signaling during ELD induction reduces pulmonary type 2 immune responses, eosinophil activation and alleviates lung lacunarity. In conclusion, we demonstrate that IL-33 signaling is essentially involved in MF-induced ELD development.

opencc-zeroMar 2024View details →

opencc-by-4.0Nov 2021View details →

Outdoor air pollution impacts chronic obstructive pulmonary disease deaths in South Asia and China: a systematic review and meta-analysis

Background: Chronic obstructive pulmonary disease (COPD) is among leading causes of death globally. Exposure to outdoor pollution is an important cause for increased mortality and morbidity. This study presents a systemic review regarding the impact of outdoor pollution on COPD mortality in South Asia and China. Methods: A systematic search was conducted from 1990 to June 30th 2020 in English electronic databases: PubMed, Google Scholar and CDSR (Cochrane Database of Systematic Reviews) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following terms were used: Chronic Obstructive Pulmonary disease OR COPD OR Chronic Bronchitis OR Emphysema OR COPD Deaths OR Chronic Obstructive Lung Disease OR Airflow Obstruction OR Chronic Airflow Obstruction OR Airflow Obstruction, Chronic OR Bronchitis, Chronic AND Mortality OR Death OR Deceased AND Outdoor pollution, ambient pollution was conducted. Results: Out of 1899 papers screened only 17 were found eligible to be included. Subjects with COPD exposed to higher levels of outdoor air pollution had a 49% higher risk of death as compared to COPD subjects exposed to lower levels of outdoor air pollution. When taking common air pollutants individually into consideration, PM10 had an odds ratio (OR) of 1.99 respectively at CI 95%, whereas SO2 had OR of 1.8 at 95% CI, and NO2 had an OR of 1.23 OR at 95% CI. These values suggest that there is an effect of outdoor pollution on COPD but not to a significant level. Conclusion: Despite heterogeneity across selected studies, individuals exposed to outdoor pollutants were found to be at risk of COPD mortality. Though it appears to have risk, COPD mortality was not significantly associated with outdoor pollutants. Controlling air pollution can substantially decrease the risk of COPD in South Asia and China. Further researches including more prospective and longitudinal studies are urgently needed in COPD sub-groups.

opencc-by-4.0Mar 2022View details →

Metabolic and lipidomic data of patients with idiopathic pulmonary fibrosis and healthy volunteers

The metabolomic / lipidomic datasets used in the manuscript draft entiteld:  "Changes in Serum Metabolomics in Idiopathic Pulmonary Fibrosis and effect of approved antifibrotic medication" by Benjamin Seeliger, Alfonso Carleo, Pedro David Wendel-Garcia, Jan Fuge, Ana Montes Worboys, Sven Schuchardt, Maria Molina-Molina and Antje Prasse Data is untransformed and missing data were imputated. All values are in µmol/L.

opencc-by-4.0Jan 2022View details →

Mice MR Images (lungs and pulmonary metastases)

55 mice were imaged after injection on a 7T Bruker BioSpec system equipped with a gradient coil of 660 mT/m maximum strength and 110 μs rise time. 27 mice with Crispr/Cas9 il34 KO gene and 28 controls. Animals were imaged every week: from day 6 to day 32 post-implantation for control mice  and  from day 8 till their condition deteriorated (up to day 141 at most) for il34 mice. Two additional healthy mice were scanned 3 times, two times without repositioning and one time after waking them up in order to evaluate the reproducibility of the lungs’ segmentations. The balanced Steady State Free Precession (bSSFP) sequence was chosen, as it has previously been shown that high tumor contrast can be obtained in the brain and in the liver (<a href="https://doi.org/10.1002/jmri.21449">https://doi.org/10.1002/jmri.21449</a> ; <a href="https://doi.org/10.1002/jmri.22593">https://doi.org/10.1002/jmri.22593</a> ; <a href="https://doi.org/10.1002/jmri.24688">https://doi.org/10.1002/jmri.24688</a>). Combined with the Self-Gating (SG) method, it enables to delete echoes affected by motion and consequently obtain abdominal images without motion artifact (<a href="https://doi.org/10.1002/jmri.24688">https://doi.org/10.1002/jmri.24688</a>).  Corresponding masks were manually drawn around the lungs on every slice of 185 3D images; masks were manually drawn around the pulmonary metastases on every slice of 62 3D images containing metastases. Both tasks were performed by two different investigators. Link to the code : https://github.com/cbib/DeepMeta

opencc-by-4.0Apr 2024View details →

Development of a desorption electrospray ionization –multiple-reaction-monitoring mass spectrometry (DESI-MRM) workflow for spatially mapping oxylipins in pulmonary tissue

Data from desorption electrospray ionization mass spectrometry – multiple-reaction-monitoring mass spectrometry (DESI-MRM) analysis of oxylipins in guinea pig lung tissue following in vivo exposure to house dust mite extract. Data are provided as Waters *.raw data folders, each incuding an 'Analyte .txt' file, which is generated from processing within MassLynx (Waters). The 'ion_library.txt' file includes details about the MRM transitions and is required for processing the data with quantMSImageR (<a href="https://github.com/targeted-lipidomics/quantMSImageR">https://github.com/targeted-lipidomics/quantMSImageR</a>).

opencc-by-4.0Apr 2019View details →

Fig. 2. Pulmonary hydatid cyst from a 3 year old female water buffalo from a in The first report of hydatid disease (Echinococcus granulosus) in an Australian water buffalo (Bubalus bubalis)

Fig. 2. Pulmonary hydatid cyst from a 3 year old female water buffalo from a farm in New South Wales, Australia.

opencc-by-4.0Dec 2019View details →

Fig. 3 in First report of pulmonary cysticercosis caused by Taenia crassiceps in a Cape fur seal (Arctocephalus pusillus)

Fig. 3. Alignment result for the partial sequence of the COX 1 gene (fur seal) with an exemplary T. crassiceps COX 1 gene sequence (accession no. KY321321.1), obtained from NCBI BLASTN tool. Homology was 100% (query: KY321321.1, sbject: herein obtained sequence).

Figure 4 in Іnfluence of some heavy metals to the pulmonary and direct diffusive respiration of the great ramshorn Planorbarius corneus allospecies (Mollusca: Gastropoda: Planorbidae) from the Ukrainian river system

Figure 4. Photo of habitat Planorbarius corneus from Sula River (Romny, Sumy region) in 2021 (Photos: Yuliia V. Babych).

Figure 2 in Іnfluence of some heavy metals to the pulmonary and direct diffusive respiration of the great ramshorn Planorbarius corneus allospecies (Mollusca: Gastropoda: Planorbidae) from the Ukrainian river system

Figure 2. Map showing the type localities of Planorbarius corneus s. lato allospecies: black triangle – «western»; black square – «eastern».

Figure 3 in Іnfluence of some heavy metals to the pulmonary and direct diffusive respiration of the great ramshorn Planorbarius corneus allospecies (Mollusca: Gastropoda: Planorbidae) from the Ukrainian river system

Figure 3. Photo of habitat Planorbarius corneus from Hnyla River (Horodnytsia village, Ternopil region) in 2021 (Photos: Yuliia V. Babych).

Figure 1 in Іnfluence of some heavy metals to the pulmonary and direct diffusive respiration of the great ramshorn Planorbarius corneus allospecies (Mollusca: Gastropoda: Planorbidae) from the Ukrainian river system

Figure 1. Shells of Planorbarius corneus s. lato. (A – allospecies "western", B – allospecies "eastern"): 1 – top view; 2 – bottom view; 3 – side view. Scale bars: 10 mm. (Photos: Yuliia V. Babych).