cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7543826
title: Dataset related to the article "AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients"

description: This record contains partial raw data related to the article “AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients” <strong>Purpose: </strong>To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients' risk stratification. <strong>Material and methods: </strong>In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web-mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). <strong>Results: </strong>The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816-0.867) on wave 1 and was used to build a 0-100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402-0.8766). <strong>Conclusions: </strong>AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.
DOI: 10.5281/zenodo.7543827
title: Dataset related to the article "AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients"

description: This record contains partial raw data related to the article “AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients” <strong>Purpose: </strong>To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients' risk stratification. <strong>Material and methods: </strong>In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web-mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). <strong>Results: </strong>The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816-0.867) on wave 1 and was used to build a 0-100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402-0.8766). <strong>Conclusions: </strong>AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.
DOI: 10.25403/upresearchdata.21940529
title: Baleen oestrogen and progesterone metabolite concentrations and stable isotope data

description: Summary data sheet containing all stable isotope data (carbon and nitrogen) as well as baleen oestrogen and progesterone metabolite concentrations. A moving average for each was also calculated and is available on the sheet (k=3). Baseline progesterone levels are also listed. Data was obtained by subsampling baleen plates at 1 or 2cm intervals and each subsample was sent for the appropriate analysis to generate data. "Year" references the approximate year during which the particular subsample's baleen was grown.
DOI: 10.25403/upresearchdata.21940529.v1
title: Baleen oestrogen and progesterone metabolite concentrations and stable isotope data

description: Summary data sheet containing all stable isotope data (carbon and nitrogen) as well as baleen oestrogen and progesterone metabolite concentrations. A moving average for each was also calculated and is available on the sheet (k=3). Baseline progesterone levels are also listed. Data was obtained by subsampling baleen plates at 1 or 2cm intervals and each subsample was sent for the appropriate analysis to generate data. "Year" references the approximate year during which the particular subsample's baleen was grown.
DOI: 10.5517/ccdc.csd.cc24jsm3
title: CCDC 1982345: Experimental Crystal Structure Determination

description: Related Article: Yipin Zhang, Xia Ge, Hongjian Lu, Guigen Li|2021|Angew.Chem.,Int.Ed.|60|1845|doi:10.1002/anie.202010974
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25h0rg
title: CCDC 2010435: Experimental Crystal Structure Determination

description: Related Article: Sebastian Termühlen, Jonas Blumenberg, Alexander Hepp, Constantin G. Daniliuc, F. Ekkehardt Hahn|2021|Angew.Chem.,Int.Ed.|60|2599|doi:10.1002/anie.202010988
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25h0sh
title: CCDC 2010436: Experimental Crystal Structure Determination

description: Related Article: Sebastian Termühlen, Jonas Blumenberg, Alexander Hepp, Constantin G. Daniliuc, F. Ekkehardt Hahn|2021|Angew.Chem.,Int.Ed.|60|2599|doi:10.1002/anie.202010988
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25x105
title: CCDC 2022936: Experimental Crystal Structure Determination

description: Related Article: Kristers Ozols, Shunsuke Onodera, Łukasz Woźniak, Nicolai Cramer|2021|Angew.Chem.,Int.Ed.|60|655|doi:10.1002/anie.202011140
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25x116
title: CCDC 2022937: Experimental Crystal Structure Determination

description: Related Article: Kristers Ozols, Shunsuke Onodera, Łukasz Woźniak, Nicolai Cramer|2021|Angew.Chem.,Int.Ed.|60|655|doi:10.1002/anie.202011140
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25x127
title: CCDC 2022938: Experimental Crystal Structure Determination

description: Related Article: Kristers Ozols, Shunsuke Onodera, Łukasz Woźniak, Nicolai Cramer|2021|Angew.Chem.,Int.Ed.|60|655|doi:10.1002/anie.202011140
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25x138
title: CCDC 2022939: Experimental Crystal Structure Determination

description: Related Article: Kristers Ozols, Shunsuke Onodera, Łukasz Woźniak, Nicolai Cramer|2021|Angew.Chem.,Int.Ed.|60|655|doi:10.1002/anie.202011140
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc25x149
title: CCDC 2022940: Experimental Crystal Structure Determination

description: Related Article: Kristers Ozols, Shunsuke Onodera, Łukasz Woźniak, Nicolai Cramer|2021|Angew.Chem.,Int.Ed.|60|655|doi:10.1002/anie.202011140
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1nh4s2
title: CCDC 1533904: Experimental Crystal Structure Determination

description: Related Article: Brendan G. Dwyer, Chao Wang, Daniel Abegg, Brittney Racioppo, Nan Qiu, Zhensheng Zhao, Dany Pechalrieu, Anton Shuster, Dominic G. Hoch, Alexander Adibekian|2021|Angew.Chem.,Int.Ed.|60|3071|doi:10.1002/anie.202011163
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc1nh4t3
title: CCDC 1533905: Experimental Crystal Structure Determination

description: Related Article: Brendan G. Dwyer, Chao Wang, Daniel Abegg, Brittney Racioppo, Nan Qiu, Zhensheng Zhao, Dany Pechalrieu, Anton Shuster, Dominic G. Hoch, Alexander Adibekian|2021|Angew.Chem.,Int.Ed.|60|3071|doi:10.1002/anie.202011163
description: Related Article: Brendan G. Dwyer, Chao Wang, Daniel Abegg, Brittney Racioppo, Nan Qiu, Zhensheng Zhao, Dany Pechalrieu, Anton Shuster, Dominic G. Hoch, and Alexander Adibekian|2020|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d8zg4
title: CCDC 2213166: Experimental Crystal Structure Determination

description: Related Article: Gregor Talajić, Edi Topić, Jerko Meštrović, Nikola Cindro|2022|J.Org.Chem.|87|16054|doi:10.1021/acs.joc.2c02200
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7541883
title: Dataset related to the article "Cardiac magneticresonanceforprophylactic implantable-cardioverter defibrillator therapy international study:prognosticvalueofcardiac magnetic resonance-derivedrightventricular parameters substudy"

description: This record contains partial raw data related to the article “Cardiac magneticresonanceforprophylactic implantable-cardioverter defibrillator therapy international study:prognosticvalueofcardiac magnetic resonance-derivedrightventricular parameters substudy" <strong>Aims: </strong>Right ventricular (RV) systolic dysfunction (RVSD) has been recognized as an important determinant of outcomes in heart failure (HF) patients. However, assessment of RV volumes is challenging as the geometric complexity of the RV prohibits quantitative assessment by traditional 2D-echocardiography. Cardiac magnetic resonance (CMR) is the gold standard for RV chamber quantification with its high spatial resolution, precise definition of anatomy, and excellent reproducibility. In this study, we sought to investigate the incremental prognostic value of CMR-derived RV volumetric parameters in a large cohort of HF patients with reduced ejection fraction (HFrEF). <strong>Methods and Results: </strong>Study cohort comprised of patients enrolled in the CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DefibrillAtor ThErapy registry who had HFrEF and had simultaneous baseline CMR and echocardiography<br> (n=2449). RVSD was defined as RV ejection fraction (RVEF) &lt;45%. Kaplan–Meier curves and cox regression were used to investigate the association between RVSD and all-cause mortality (ACM). Mean age was 59.8±14.0 years, 42.0% were female, and mean left ventricular ejection fraction (LVEF) was 34.0±10.8. Median follow-up was 959 days (interquartile range: 560–1590). RVSD was present in 936 (38.2%) and was an independent predictor of ACM (adjusted hazard ratio=1.44; 95%CI[1.09–1.91]; P=0.01). On subgroup analyses,the prognostic value of RVSD was more pronounced in NYHA I/II than in NYHAIII/IV, in LVEF &lt;35% than in LVEF ≥35%, and in patients with renal dysfunction when compared to those with normal renal function. <strong>Conclusions: </strong>RV dysfunction is an independent predictor of all-cause mortality in a large cohort of patients with HFrEF, with the prognostic value of RV dysfunction being more pronounced in select subgroups; likely reflecting the importance of RV function in the early stages of HF progression.
DOI: 10.5281/zenodo.7541884
title: Dataset related to the article "Cardiac magneticresonanceforprophylactic implantable-cardioverter defibrillator therapy international study:prognosticvalueofcardiac magnetic resonance-derivedrightventricular parameters substudy"

description: This record contains partial raw data related to the article “Cardiac magneticresonanceforprophylactic implantable-cardioverter defibrillator therapy international study:prognosticvalueofcardiac magnetic resonance-derivedrightventricular parameters substudy" <strong>Aims: </strong>Right ventricular (RV) systolic dysfunction (RVSD) has been recognized as an important determinant of outcomes in heart failure (HF) patients. However, assessment of RV volumes is challenging as the geometric complexity of the RV prohibits quantitative assessment by traditional 2D-echocardiography. Cardiac magnetic resonance (CMR) is the gold standard for RV chamber quantification with its high spatial resolution, precise definition of anatomy, and excellent reproducibility. In this study, we sought to investigate the incremental prognostic value of CMR-derived RV volumetric parameters in a large cohort of HF patients with reduced ejection fraction (HFrEF). <strong>Methods and Results: </strong>Study cohort comprised of patients enrolled in the CarDiac MagnEtic Resonance for Primary Prevention Implantable CardioVerter DefibrillAtor ThErapy registry who had HFrEF and had simultaneous baseline CMR and echocardiography<br> (n=2449). RVSD was defined as RV ejection fraction (RVEF) &lt;45%. Kaplan–Meier curves and cox regression were used to investigate the association between RVSD and all-cause mortality (ACM). Mean age was 59.8±14.0 years, 42.0% were female, and mean left ventricular ejection fraction (LVEF) was 34.0±10.8. Median follow-up was 959 days (interquartile range: 560–1590). RVSD was present in 936 (38.2%) and was an independent predictor of ACM (adjusted hazard ratio=1.44; 95%CI[1.09–1.91]; P=0.01). On subgroup analyses,the prognostic value of RVSD was more pronounced in NYHA I/II than in NYHAIII/IV, in LVEF &lt;35% than in LVEF ≥35%, and in patients with renal dysfunction when compared to those with normal renal function. <strong>Conclusions: </strong>RV dysfunction is an independent predictor of all-cause mortality in a large cohort of patients with HFrEF, with the prognostic value of RV dysfunction being more pronounced in select subgroups; likely reflecting the importance of RV function in the early stages of HF progression.
DOI: 10.5281/zenodo.7541833
title: Dataset related to the article "Prediction of myocardial blood flow under stress conditions by means of a computational model"

description: This record contains raw data related to the article “Prediction of myocardial blood flow under stress conditions by means<br> of a computational model” <strong>Purpose. </strong>Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. <strong>Methods. </strong>A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBF<sub>CTP</sub>) maps. The relative error between the computational MBF under stress conditions (MBF<sub>COMP</sub>) and MBF<sub>CTP</sub> was evaluated to assess the accuracy of the proposed computational model. <strong>Results.</strong> Applying our method to 9 patients (4 control subjects without ischemica vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBF<sub>COMP</sub> and MBF<sub>CTP</sub>. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. <strong>Conclusion. </strong>The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e. the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.
DOI: 10.5281/zenodo.7541834
title: Dataset related to the article "Prediction of myocardial blood flow under stress conditions by means of a computational model"

description: This record contains raw data related to the article “Prediction of myocardial blood flow under stress conditions by means<br> of a computational model” <strong>Purpose. </strong>Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps. <strong>Methods. </strong>A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBF<sub>CTP</sub>) maps. The relative error between the computational MBF under stress conditions (MBF<sub>COMP</sub>) and MBF<sub>CTP</sub> was evaluated to assess the accuracy of the proposed computational model. <strong>Results.</strong> Applying our method to 9 patients (4 control subjects without ischemica vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBF<sub>COMP</sub> and MBF<sub>CTP</sub>. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%. <strong>Conclusion. </strong>The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e. the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.
DOI: 10.5281/zenodo.7541451
title: Dataset related to the article "Reprogramming of dermal fibroblasts from a Duchenne muscular dystrophy patient carrying a deletion of exons 45–50 into an induced pluripotent stem cell line (CCMi005-A)"

description: This record contains raw data related to the article " "Reprogramming of dermal fibroblasts from a Duchenne muscular dystrophy patient carrying a deletion of exons 45–50 into an induced pluripotent stem cell line (CCMi005-A)" Abstract Duchenne muscular dystrophy (DMD) is an X-linked syndrome that affects skeletal and cardiac muscle and is caused by mutation of the dystrophin gene. Induced pluripotent stem cells (iPSCs) were generated from dermal fibroblasts by electroporation with episomal vectors containing the reprogramming factors (OCT4, SOX2, LIN28, KLF4, and L-MYC). The donor carried an out-of-frame deletion of exons 45–50 of the dystrophin gene. The established iPSC line exhibited normal morphology, expressed pluripotency markers, had normal karyotype and possessed trilineage differentiation potential.
description: Founding: Fondazione IEO-CCM; Italian Ministry of Health-Ricerca Corrente MPP5A: Advanced cell models of cardiomyopathies; Fondazione Umberto Veronesi; Telethon-Unione Italiana Lotta alla Distrofia Muscolare Clinical Projects 2019 GUP19012