cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.25196990
title: Supplementary materials: Assessment of second primary malignancies among treated and untreated patients with chronic lymphocytic leukemia using real-world data from the USA

description: <b>These are peer-reviewed supplementary figures and tables for the article '</b><b>Treatment patterns and economic burden of bacterial vaginosis among commercially insured women in the USA</b><b>' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><b>Supplementary Figure 1: </b>Timing of events for the treated and untreated subgroups<b>Supplementary Figure 2:</b> The covariate balance between the treated and untreated subgroups<b>Supplementary Table 1: </b>Procedure and NDC codes for CLL treatments<b>Supplementary Table 2: </b>Diagnosis codes for SPMs<b>Aim: </b>Improved management of chronic lymphocytic leukemia (CLL) has resulted in a growing populationof CLL survivors; these patients have a higher risk of developing second primary malignancies (SPMs) versusthe general population. This retrospective cohort study aims to assess the timing, frequency, incidence andtypes of SPMs in treated and untreated patients with CLL in the USA, using the Surveillance, Epidemiology,and End Results (SEER) Medicare database, which links a nationally representative cancer registry withMedicare claims data. <b>Patients & methods:</b> Patients aged ≥66 years with newly diagnosed CLL between1 January 2010 and 31 December 2016, who were enrolled in Parts A and B of Medicare for ≥12 monthspre-diagnosis of CLL were selected from the database. Patients were assessed for ≥36 months until the endof continuous enrollment in Medicare Parts A, B and D, a switch to a health maintenance organization,death, or end of the study period (December 2019). <b>Results:</b> Of 3053 patients included in the analyses,620 (20.3%) were treated and 2433 (79.7%) were untreated within 36 months of diagnosis. Overall,638 (20.9%) patients developed a SPM, 26.8% of patients in the treated cohort and 19.4% of patientsin the untreated cohort. The most common SPMs for both cohorts were squamous cell carcinoma andacute myeloid leukemia. Among the 166 treated patients who developed a SPM, a greater proportiondeveloped their first SPM after treatment initiation versus those who developed their first SPM priorto treatment initiation (p &lt; 0.001). A significantly lower percentage of patients who received targetedtherapy developed a SPM (p &lt; 0.05) versus patients treated with anti-CD20 + chemotherapy. Conclusion:Findings indicate that treatment type and timing can affect SPM development in patients with CLL.Combined with previous findings, this can help inform best practices in monitoring for SPM in patientswith CLL.
DOI: 10.6084/m9.figshare.25196990.v1
title: Supplementary materials: Assessment of second primary malignancies among treated and untreated patients with chronic lymphocytic leukemia using real-world data from the USA

description: <b>These are peer-reviewed supplementary figures and tables for the article '</b><b>Treatment patterns and economic burden of bacterial vaginosis among commercially insured women in the USA</b><b>' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><b>Supplementary Figure 1: </b>Timing of events for the treated and untreated subgroups<b>Supplementary Figure 2:</b> The covariate balance between the treated and untreated subgroups<b>Supplementary Table 1: </b>Procedure and NDC codes for CLL treatments<b>Supplementary Table 2: </b>Diagnosis codes for SPMs<b>Aim: </b>Improved management of chronic lymphocytic leukemia (CLL) has resulted in a growing populationof CLL survivors; these patients have a higher risk of developing second primary malignancies (SPMs) versusthe general population. This retrospective cohort study aims to assess the timing, frequency, incidence andtypes of SPMs in treated and untreated patients with CLL in the USA, using the Surveillance, Epidemiology,and End Results (SEER) Medicare database, which links a nationally representative cancer registry withMedicare claims data. <b>Patients & methods:</b> Patients aged ≥66 years with newly diagnosed CLL between1 January 2010 and 31 December 2016, who were enrolled in Parts A and B of Medicare for ≥12 monthspre-diagnosis of CLL were selected from the database. Patients were assessed for ≥36 months until the endof continuous enrollment in Medicare Parts A, B and D, a switch to a health maintenance organization,death, or end of the study period (December 2019). <b>Results:</b> Of 3053 patients included in the analyses,620 (20.3%) were treated and 2433 (79.7%) were untreated within 36 months of diagnosis. Overall,638 (20.9%) patients developed a SPM, 26.8% of patients in the treated cohort and 19.4% of patientsin the untreated cohort. The most common SPMs for both cohorts were squamous cell carcinoma andacute myeloid leukemia. Among the 166 treated patients who developed a SPM, a greater proportiondeveloped their first SPM after treatment initiation versus those who developed their first SPM priorto treatment initiation (p &lt; 0.001). A significantly lower percentage of patients who received targetedtherapy developed a SPM (p &lt; 0.05) versus patients treated with anti-CD20 + chemotherapy. Conclusion:Findings indicate that treatment type and timing can affect SPM development in patients with CLL.Combined with previous findings, this can help inform best practices in monitoring for SPM in patientswith CLL.
DOI: 10.5281/zenodo.10640951
title: Wide real-life data supports reduced sensitivity of antigen tests for Omicron SARS-CoV-2 infections

description: Background: with the continuous spread of new SARS-CoV-2 variants of concern (VOC), monitoring of diagnostic test performances is mandatory. This study aimed at evaluating the changes in the accuracy of antigen diagnostic tests (ADT) along Delta to Omicron VOCs transition, exploring also N protein mutations possibly affecting ADT sensitivity and assessing the best sampling site (nose or mouth) for the diagnosis of Omicron infections. 

Methods: 5,175 patients referring to IRCCS Sacro Cuore Don Calabria Hospital (Italy) since October 1ST 2021 to July 15TH 2022 were included in the study. Inclusion criteria were SARS-CoV-2 ADT combined to reverse transcriptase polymerase chain reaction (RT-PCR) swab test, performed in the same day. For sampling site analysis, 61 subjects verified as positive to SARS-CoV-2 during the Omicron period were prospectively recruited for nose and mouth swab analysis by RT-PCR. Next Generation Sequencing data was obtained to evaluate the different sublineages.

Results: using RT-PCR as reference, the overall sensitivity of the ADT decreased from 64% in the Delta period to 33.3% in the Omicron period. This decrease was highly statistically significant (p<0.0001) and no difference in viral load levels were detected. The nose site presented significantly higher viral load respect to the mouth during the Omicron wave. 

Conclusions: The reduced detection rate of Omicron infections by ADT should be considered in the global testing strategy, to preserve accurate diagnosis across the SARS-CoV-2 changing variants.
DOI: 10.5281/zenodo.10640952
title: Wide real-life data supports reduced sensitivity of antigen tests for Omicron SARS-CoV-2 infections

description: Background: with the continuous spread of new SARS-CoV-2 variants of concern (VOC), monitoring of diagnostic test performances is mandatory. This study aimed at evaluating the changes in the accuracy of antigen diagnostic tests (ADT) along Delta to Omicron VOCs transition, exploring also N protein mutations possibly affecting ADT sensitivity and assessing the best sampling site (nose or mouth) for the diagnosis of Omicron infections. 

Methods: 5,175 patients referring to IRCCS Sacro Cuore Don Calabria Hospital (Italy) since October 1ST 2021 to July 15TH 2022 were included in the study. Inclusion criteria were SARS-CoV-2 ADT combined to reverse transcriptase polymerase chain reaction (RT-PCR) swab test, performed in the same day. For sampling site analysis, 61 subjects verified as positive to SARS-CoV-2 during the Omicron period were prospectively recruited for nose and mouth swab analysis by RT-PCR. Next Generation Sequencing data was obtained to evaluate the different sublineages.

Results: using RT-PCR as reference, the overall sensitivity of the ADT decreased from 64% in the Delta period to 33.3% in the Omicron period. This decrease was highly statistically significant (p<0.0001) and no difference in viral load levels were detected. The nose site presented significantly higher viral load respect to the mouth during the Omicron wave. 

Conclusions: The reduced detection rate of Omicron infections by ADT should be considered in the global testing strategy, to preserve accurate diagnosis across the SARS-CoV-2 changing variants.
DOI: 10.5281/zenodo.10590046
title: Brain Deformation Data

description: This dataset comprises spatio-temporal brain deformation data from one of the subjects in a study on brain tissue volumetric strain measurements using the Displacement Encoding with Stimulated Echoes (DENSE) magnetic resonance imaging method. The study involved 8 healthy subjects, aged 27 ± 6 years and implemented a retrospectively-gated version of DENSE for more efficient acquisition. The main goal of the dataset acquisition was the investigation of the reliability and repeatability of the proposed DENSE method through repeated measurements. The overall study, approved by the Ethical Review Board of University Medical Center Utrecht, aimed to validate the suitability of DENSE for the assessment of brain tissue pulsation as a physiological marker and for the investigation of intracranial volume dynamics [1]. The dataset has also been used to investigate the value of head capacitance measurements as a non-invasive biomarker for cranial compliance, e.g., for diagnostic purposes [2].Because of privacy concerns, the data was masked to only reveal the brain and CSF volume. 

[1] Adams, A.L., Viergever, M.A., Luijten, P.R. and Zwanenburg, J.J., 2020. Validating faster DENSE measurements of cardiac-induced brain tissue expansion as a potential tool for investigating cerebral microvascular pulsations. Neuroimage, 208, p.116466. DOI: https://doi.org/10.1016/j.neuroimage.2019.116466

[2] Karimi, F., Neufeld, E., Fallahi, A., Boraschi, A., Zwanenburg, J.J., Spiegelberg, A., Kurtcuoglu, V. and Kuster, N., 2023. Theory for a non-invasive diagnostic biomarker for craniospinal diseases. NeuroImage: Clinical, 37, p.103280. DIO: https://doi.org/10.1016/j.nicl.2022.103280
DOI: 10.5281/zenodo.10590047
title: Brain Deformation Data

description: This dataset comprises spatio-temporal brain deformation data from one of the subjects in a study on brain tissue volumetric strain measurements using the Displacement Encoding with Stimulated Echoes (DENSE) magnetic resonance imaging method. The study involved 8 healthy subjects, aged 27 ± 6 years and implemented a retrospectively-gated version of DENSE for more efficient acquisition. The main goal of the dataset acquisition was the investigation of the reliability and repeatability of the proposed DENSE method through repeated measurements. The overall study, approved by the Ethical Review Board of University Medical Center Utrecht, aimed to validate the suitability of DENSE for the assessment of brain tissue pulsation as a physiological marker and for the investigation of intracranial volume dynamics [1]. The dataset has also been used to investigate the value of head capacitance measurements as a non-invasive biomarker for cranial compliance, e.g., for diagnostic purposes [2].Because of privacy concerns, the data was masked to only reveal the brain and CSF volume. 

[1] Adams, A.L., Viergever, M.A., Luijten, P.R. and Zwanenburg, J.J., 2020. Validating faster DENSE measurements of cardiac-induced brain tissue expansion as a potential tool for investigating cerebral microvascular pulsations. Neuroimage, 208, p.116466. DOI: https://doi.org/10.1016/j.neuroimage.2019.116466

[2] Karimi, F., Neufeld, E., Fallahi, A., Boraschi, A., Zwanenburg, J.J., Spiegelberg, A., Kurtcuoglu, V. and Kuster, N., 2023. Theory for a non-invasive diagnostic biomarker for craniospinal diseases. NeuroImage: Clinical, 37, p.103280. DIO: https://doi.org/10.1016/j.nicl.2022.103280
DOI: 10.6084/m9.figshare.25197005.v1
title: Supplementary data: A US national update of health conditionprevalence among privately-insuredautistic adults

description: <b>This is a peer-reviewed supplementary table for the article '</b><b>A US national update of health condition </b><b>prevalence among privately-insured </b><b>autistic adults' published in the</b><b> </b><b><i>Journal of Comparative Effectiveness Research</i></b><b>.</b><b>Supplementary Table 1:</b> Study definitions of mental and physical health conditions using Healthcare Cost and Utilization Project (HCUP) Beta Multilevel Clinical Classification Software (CCS) or specific International Classification of Diseases, 10<sup>th</sup> edition (ICD-10) diagnosis codes.<b>Aim: </b>Previous research using state or regional samples has shown that autistic adults have a higherprevalence of health conditions in comparison to the general population. <b>Methods: </b>To build upon thisimportant previous research, we conducted a cross-sectional retrospective study of 2019–2020 healthcareclaims to determine the prevalence of conditions in a US national sample of privately insured autistic adults(n = 30,258) and an age- and sex-matched population comparison (n = 60,516) group of adults withoutautism diagnoses.<b> Results:</b> Like previous studies, we found that autistic adults had significantly greaterodds of most mental and physical health conditions. However, our prevalence estimates differed fromprevious studies for several mental and physical health conditions. For example, our sample of autisticadults had higher prevalence of anxiety disorders (55%) and attention deficit hyperactivity disorders(34%), but lower prevalence of asthma (9%) and sleep disorders (3%) than previous studies. <b>Discussion</b><b>& conclusion: </b>Our use of a large US national sample, more recent healthcare claims data, and differentmethods for identifying health conditions may have contributed to these differences. Our findings alerthealthcare providers and policymakers to the health conditions most common among the growingpopulation of autistic adults. We hope these findings lead to improved screening and management ofthese conditions, inform initiatives to improve access to healthcare, and guide future funding.
DOI: 10.5281/zenodo.10406280
title: MedShapeNetCore
DOI: 10.5281/zenodo.10606436
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.  If you use the dataset in your research, please also cite the original datasets (which can be found using command <!python -m MedShapeNetCore info>), besides MedShapeNet.
DOI: 10.5281/zenodo.10609965
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.  If you use the dataset in your research, please also cite the original datasets (which can be found using command <!python -m MedShapeNetCore info>), besides MedShapeNet.

 

You can search the database using anatomy nomenclature, such as 'liver', 'aorta', 'skull' etc.  Fore more commands, visit the Github Repository.

python -m MedShapeNetCore search_by_organ ORGAN
DOI: 10.5281/zenodo.10423181
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.
DOI: 10.5281/zenodo.10567382
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.
DOI: 10.5281/zenodo.10568941
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.
DOI: 10.5281/zenodo.10617451
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.  If you use the dataset in your research, please also cite the original datasets (which can be found using command <!python -m MedShapeNetCore info>), besides MedShapeNet.

 

Make sure to check out the MedShapeNetCore Show Case.

 

@article{li2023medshapenet,
  title={MedShapeNet--A Large-Scale Dataset of 3D Medical Shapes for Computer Vision},
  author={Li, Jianning and Zhou, Zongwei and Yang, Jiancheng and others},
  journal={arXiv preprint arXiv:2308.16139},
  year={2023}
}

 

To install the MedShapeNetCore python package:

 pip install MedShapeNetCore

You can search the database using anatomy nomenclature, such as liver, aorta, skull, instrument etc.  Fore more commands, visit the Github Repository.

python -m MedShapeNetCore search_by_organ ORGANOr, search MedShapeNetCore using disease keywords, such as 'tumor' python -m MedShapeNetCore search_by_disease tumor

You can also directly download .stl files using the following command (replace ORGAN with the organ you want to search, e.g., liver, skull):

python -m MedShapeNetCore search_and_download ORGAN

Check the available .npz files:

python -m MedShapeNetCore info

Download .npz files to be used in python:

python -m MedShapeNetCore download FaceVRImport the python packages to load, visualize and process the .npz files:from MedShapeNetCore.MedShapeNetCore import MyDict,MSNLoader,MSNVisualizer,MSNSaver,MSNTransformer
DOI: 10.5281/zenodo.10623482
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.  If you use the dataset in your research, please also cite the original datasets (which can be found using command <!python -m MedShapeNetCore info>), besides MedShapeNet.

 

Make sure to check out the MedShapeNetCore Show Case.

 

@article{li2023medshapenet,
  title={MedShapeNet--A Large-Scale Dataset of 3D Medical Shapes for Computer Vision},
  author={Li, Jianning and Zhou, Zongwei and Yang, Jiancheng and others},
  journal={arXiv preprint arXiv:2308.16139},
  year={2023}
}

 

To install the MedShapeNetCore python package:

 pip install MedShapeNetCore

You can search the database using anatomy nomenclature, such as liver, aorta, skull, instrument etc.  Fore more commands, visit the Github Repository.

python -m MedShapeNetCore search_by_organ ORGAN

You can also directly download .stl files using the following command (replace ORGAN with the organ you want to search, e.g., liver, skull):

python -m MedShapeNetCore search_and_download ORGAN

Check the available .npz files:

python -m MedShapeNetCore info

Download .npz files to be used in python:

python -m MedShapeNetCore download FaceVRImport the python packages to load, visualize and process the .npz files:from MedShapeNetCore.MedShapeNetCore import MyDict,MSNLoader,MSNVisualizer,MSNSaver,MSNTransformer
DOI: 10.5281/zenodo.10406279
title: MedShapeNetCore

description: MedShapeNetCore is a subset of MedShapeNet, containing more lightweight 3D anatomical shapes in the format of mask, point cloud and mesh. The shape data are stored as numpy arrays in nested dictonaries in npz format (Zenodo). This API provides means to downloading, accessing and processing the shape data via Python, which integrates MedShapeNetCore seamless into Python-based machine learning workflows. For details, visit the Github Repository.  If you use the dataset in your research, please also cite the original datasets (which can be found using command <!python -m MedShapeNetCore info>), besides MedShapeNet.

 

Make sure to check out the MedShapeNetCore Show Case for the latest update.

 

@article{li2023medshapenet,
  title={MedShapeNet--A Large-Scale Dataset of 3D Medical Shapes for Computer Vision},
  author={Li, Jianning and Zhou, Zongwei and Yang, Jiancheng and others},
  journal={arXiv preprint arXiv:2308.16139},
  year={2023}
}

 

To install the MedShapeNetCore python package:

 pip install MedShapeNetCore

You can search the database using anatomy nomenclature, such as liver, aorta, skull, instrument etc.  Fore more commands, visit the Github Repository.

python -m MedShapeNetCore search_by_organ ORGAN

You can also directly download .stl files using the following command (replace ORGAN with the organ you want to search, e.g., liver, skull):

python -m MedShapeNetCore search_and_download ORGAN

Check the available .npz files:

python -m MedShapeNetCore info

Download .npz files to be used in python:

python -m MedShapeNetCore download FaceVRImport the python packages to load, visualize and process the .npz files:from MedShapeNetCore.MedShapeNetCore import MyDict,MSNLoader,MSNVisualizer,MSNSaver,MSNTransformer

Import shape dataloader:from MedShapeNetCore.MedShapeNetCore import BatchLoaderfacial_point=BatchLoader('facialVR','point',batch_size=2,shuffle=True)
for batch in facial_point:    print(batch.shape)

Use the fast load function to load a specified number of  samples  (2) of a given format ('point') from a dataset ('facialVR'):

msn_loader=MSNLoader()data=msn_loader.fast_load('facialVR','point',2,shuffle=True)
print(data.shape)