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Article ; Online: CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations.

Reinhold, William C / Wilson, Kelli / Elloumi, Fathi / Bradwell, Katie R / Ceribelli, Michele / Varma, Sudhir / Wang, Yanghsin / Duveau, Damien / Menon, Nikhil / Trepel, Jane / Zhang, Xiaohu / Klumpp-Thomas, Carleen / Micheal, Samuel / Shinn, Paul / Luna, Augustin / Thomas, Craig / Pommier, Yves

Cancer research

2023 Volume 83, Issue 12, Page(s) 1941–1952

Abstract: Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate ... ...

Abstract	Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate these efforts, we have developed CellMinerCDB: National Center for Advancing Translational Sciences (NCATS; https://discover.nci.nih.gov/rsconnect/cellminercdb_ncats/), which makes available activity information for 2,675 drugs and compounds, including multiple nononcology drugs and 1,866 drugs and compounds unique to the NCATS. CellMinerCDB: NCATS comprises 183 cancer cell lines, with 72 unique to NCATS, including some from previously understudied tissues of origin. Multiple forms of data from different institutes are integrated, including single and combination drug activity, DNA copy number, methylation and mutation, transcriptome, protein levels, histone acetylation and methylation, metabolites, CRISPR, and miscellaneous signatures. Curation of cell lines and drug names enables cross-database (CDB) analyses. Comparison of the datasets is made possible by the overlap between cell lines and drugs across databases. Multiple univariate and multivariate analysis tools are built-in, including linear regression and LASSO. Examples have been presented here for the clinical topoisomerase I (TOP1) inhibitors topotecan and irinotecan/SN-38. This web application provides both substantial new data and significant pharmacogenomic integration, allowing exploration of interrelationships. Significance: CellMinerCDB: NCATS provides activity information for 2,675 drugs in 183 cancer cell lines and analysis tools to facilitate pharmacogenomic research and to identify determinants of response.
MeSH term(s)	United States ; Humans ; National Center for Advancing Translational Sciences (U.S.) ; Pharmacogenetics ; Cell Line, Tumor ; Databases, Factual ; Irinotecan ; Neoplasms, Basal Cell ; Internet
Chemical Substances	Irinotecan (7673326042)
Language	English
Publishing date	2023-05-04
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	1432-1
ISSN	1538-7445 ; 0008-5472
ISSN (online)	1538-7445
ISSN	0008-5472
DOI	10.1158/0008-5472.CAN-22-2996
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Article ; Online: STRIDE: a command-line HMM-based identifier and sub-classifier of Plasmodium falciparum RIFIN and STEVOR variant surface antigen families.

Zhou, Albert E / Shah, Zalak V / Bradwell, Katie R / Munro, James B / Berry, Andrea A / Serre, David / Takala-Harrison, Shannon / O'Connor, Timothy D / Silva, Joana C / Travassos, Mark A

BMC bioinformatics

2022 Volume 23, Issue 1, Page(s) 15

Abstract: Background: RIFINs and STEVORs are variant surface antigens expressed by P. falciparum that play roles in severe malaria pathogenesis and immune evasion. These two highly diverse multigene families feature multiple paralogs, making their classification ... ...

Abstract	Background: RIFINs and STEVORs are variant surface antigens expressed by P. falciparum that play roles in severe malaria pathogenesis and immune evasion. These two highly diverse multigene families feature multiple paralogs, making their classification challenging using traditional bioinformatic methods. Results: STRIDE (STevor and RIfin iDEntifier) is an HMM-based, command-line program that automates the identification and classification of RIFIN and STEVOR protein sequences in the malaria parasite Plasmodium falciparum. STRIDE is more sensitive in detecting RIFINs and STEVORs than available PFAM and TIGRFAM tools and reports RIFIN subtypes and the number of sequences with a FHEYDER amino acid motif, which has been associated with severe malaria pathogenesis. Conclusions: STRIDE will be beneficial to malaria research groups analyzing genome sequences and transcripts of clinical field isolates, providing insight into parasite biology and virulence.
MeSH term(s)	Antigens, Protozoan ; Antigens, Surface ; Erythrocytes ; Humans ; Malaria, Falciparum ; Plasmodium falciparum/genetics ; Protozoan Proteins/genetics
Chemical Substances	Antigens, Protozoan ; Antigens, Surface ; Protozoan Proteins
Language	English
Publishing date	2022-01-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-021-04515-8
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Article: FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses.

Chung, Matthew / Adkins, Ricky S / Mattick, John S A / Bradwell, Katie R / Shetty, Amol C / Sadzewicz, Lisa / Tallon, Luke J / Fraser, Claire M / Rasko, David A / Mahurkar, Anup / Dunning Hotopp, Julie C

mSystems

2021 Volume 6, Issue 1

Abstract: Quantification tools for RNA sequencing (RNA-Seq) analyses are often designed and tested using human transcriptomics data sets, in which full-length transcript sequences are well annotated. For prokaryotic transcriptomics experiments, full-length ... ...

Abstract	Quantification tools for RNA sequencing (RNA-Seq) analyses are often designed and tested using human transcriptomics data sets, in which full-length transcript sequences are well annotated. For prokaryotic transcriptomics experiments, full-length transcript sequences are seldom known, and coding sequences must instead be used for quantification steps in RNA-Seq analyses. However, operons confound accurate quantification of coding sequences since a single transcript does not necessarily equate to a single gene. Here, we introduce FADU (Feature Aggregate Depth Utility), a quantification tool designed specifically for prokaryotic RNA-Seq analyses. FADU assigns partial count values proportional to the length of the fragment overlapping the target feature. To assess the ability of FADU to quantify genes in prokaryotic transcriptomics analyses, we compared its performance to those of eXpress, featureCounts, HTSeq, kallisto, and Salmon across three paired-end read data sets of (i)
Language	English
Publishing date	2021-01-12
Publishing country	United States
Document type	Journal Article
ISSN	2379-5077
ISSN	2379-5077
DOI	10.1128/mSystems.00917-20
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Article ; Online: Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

JAMIA open

2023 Volume 6, Issue 3, Page(s) ooad067

Abstract: Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).: Materials and methods: We developed a comprehensive approach, named Hierarchy and ... ...

Abstract	Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.
Language	English
Publishing date	2023-08-16
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooad067
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Article: Comparative transcriptomic analysis of

Castro, Joana / França, Angela / Bradwell, Katie R / Serrano, Myrna G / Jefferson, Kimberly K / Cerca, Nuno

NPJ biofilms and microbiomes

2017 Volume 3, Page(s) 3

Abstract: Bacterial vaginosis is the most common gynecological disorder affecting women of reproductive age. Bacterial vaginosis is frequently associated with the development of ... ...

Abstract	Bacterial vaginosis is the most common gynecological disorder affecting women of reproductive age. Bacterial vaginosis is frequently associated with the development of a
Language	English
Publishing date	2017-02-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	2817021-0
ISSN	2055-5008
ISSN	2055-5008
DOI	10.1038/s41522-017-0012-7
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Article ; Online: NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study

Virol J. 2022 Dec., v. 19, no. 1 p.84-84

2022

Abstract: BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 ... ...

Abstract	BACKGROUND: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53–0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47–0.56), invasive ventilation (OR: 0.59 95% CI: 0.55–0.64), AKI (OR: 0.67 95% CI: 0.63–0.72), or ECMO (OR: 0.51 95% CI: 0.36–0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.
Keywords	COVID-19 infection ; acute kidney injury ; cohort studies ; databases ; fever ; ibuprofen ; inflammation ; mortality ; pain ; pneumonia ; regression analysis ; risk ; risk reduction ; telemedicine
Language	English
Dates of publication	2022-12
Size	p. 84.
Publishing place	BioMed Central
Document type	Article ; Online
ZDB-ID	2160640-7
ISSN	1743-422X
ISSN	1743-422X
DOI	10.1186/s12985-022-01813-2
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset.

Journal of the American Medical Informatics Association : JAMIA

2022 Volume 29, Issue 7, Page(s) 1172–1182

Abstract: Objective: The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing.: Materials and methods: The National COVID Cohort Collaborative (N3C) table of laboratory ... ...

Abstract	Objective: The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. Materials and methods: The National COVID Cohort Collaborative (N3C) table of laboratory measurement data-over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. Results: Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). Discussion: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. Conclusion: The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.
MeSH term(s)	COVID-19 ; Cohort Studies ; Data Collection ; Electronic Health Records ; Humans
Language	English
Publishing date	2022-04-18
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1205156-1
ISSN	1527-974X ; 1067-5027
ISSN (online)	1527-974X
ISSN	1067-5027
DOI	10.1093/jamia/ocac054
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Article ; Online: Characteristics, Outcomes, and Severity Risk Factors Associated With SARS-CoV-2 Infection Among Children in the US National COVID Cohort Collaborative.

JAMA network open

2022 Volume 5, Issue 2, Page(s) e2143151

Abstract: Importance: Understanding of SARS-CoV-2 infection in US children has been limited by the lack of large, multicenter studies with granular data.: Objective: To examine the characteristics, changes over time, outcomes, and severity risk factors of ... ...

Abstract	Importance: Understanding of SARS-CoV-2 infection in US children has been limited by the lack of large, multicenter studies with granular data. Objective: To examine the characteristics, changes over time, outcomes, and severity risk factors of children with SARS-CoV-2 within the National COVID Cohort Collaborative (N3C). Design, setting, and participants: A prospective cohort study of encounters with end dates before September 24, 2021, was conducted at 56 N3C facilities throughout the US. Participants included children younger than 19 years at initial SARS-CoV-2 testing. Main outcomes and measures: Case incidence and severity over time, demographic and comorbidity severity risk factors, vital sign and laboratory trajectories, clinical outcomes, and acute COVID-19 vs multisystem inflammatory syndrome in children (MIS-C), and Delta vs pre-Delta variant differences for children with SARS-CoV-2. Results: A total of 1 068 410 children were tested for SARS-CoV-2 and 167 262 test results (15.6%) were positive (82 882 [49.6%] girls; median age, 11.9 [IQR, 6.0-16.1] years). Among the 10 245 children (6.1%) who were hospitalized, 1423 (13.9%) met the criteria for severe disease: mechanical ventilation (796 [7.8%]), vasopressor-inotropic support (868 [8.5%]), extracorporeal membrane oxygenation (42 [0.4%]), or death (131 [1.3%]). Male sex (odds ratio [OR], 1.37; 95% CI, 1.21-1.56), Black/African American race (OR, 1.25; 95% CI, 1.06-1.47), obesity (OR, 1.19; 95% CI, 1.01-1.41), and several pediatric complex chronic condition (PCCC) subcategories were associated with higher severity disease. Vital signs and many laboratory test values from the day of admission were predictive of peak disease severity. Variables associated with increased odds for MIS-C vs acute COVID-19 included male sex (OR, 1.59; 95% CI, 1.33-1.90), Black/African American race (OR, 1.44; 95% CI, 1.17-1.77), younger than 12 years (OR, 1.81; 95% CI, 1.51-2.18), obesity (OR, 1.76; 95% CI, 1.40-2.22), and not having a pediatric complex chronic condition (OR, 0.72; 95% CI, 0.65-0.80). The children with MIS-C had a more inflammatory laboratory profile and severe clinical phenotype, with higher rates of invasive ventilation (117 of 707 [16.5%] vs 514 of 8241 [6.2%]; P < .001) and need for vasoactive-inotropic support (191 of 707 [27.0%] vs 426 of 8241 [5.2%]; P < .001) compared with those who had acute COVID-19. Comparing children during the Delta vs pre-Delta eras, there was no significant change in hospitalization rate (1738 [6.0%] vs 8507 [6.2%]; P = .18) and lower odds for severe disease (179 [10.3%] vs 1242 [14.6%]) (decreased by a factor of 0.67; 95% CI, 0.57-0.79; P < .001). Conclusions and relevance: In this cohort study of US children with SARS-CoV-2, there were observed differences in demographic characteristics, preexisting comorbidities, and initial vital sign and laboratory values between severity subgroups. Taken together, these results suggest that early identification of children likely to progress to severe disease could be achieved using readily available data elements from the day of admission. Further work is needed to translate this knowledge into improved outcomes.
MeSH term(s)	Adolescent ; Age Distribution ; COVID-19/complications ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19/therapy ; COVID-19/virology ; Child ; Child, Preschool ; Comorbidity ; Disease Progression ; Early Diagnosis ; Female ; Humans ; Infant ; Male ; Risk Factors ; SARS-CoV-2 ; Severity of Illness Index ; Sociodemographic Factors ; Systemic Inflammatory Response Syndrome/diagnosis ; Systemic Inflammatory Response Syndrome/epidemiology ; Systemic Inflammatory Response Syndrome/therapy ; Systemic Inflammatory Response Syndrome/virology ; United States/epidemiology ; Vital Signs
Language	English
Publishing date	2022-02-01
Publishing country	United States
Document type	Journal Article ; Multicenter Study ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2021.43151
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Article: Who is pregnant? defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

medRxiv : the preprint server for health sciences

2022

Abstract: Objective: To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C).: Materials and methods: We developed a comprehensive approach, named H ierarchy and ... ...

Abstract	Objective: To define pregnancy episodes and estimate gestational aging within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and methods: We developed a comprehensive approach, named H ierarchy and rule-based pregnancy episode I nference integrated with P regnancy P rogression S ignatures (HIPPS) and applied it to EHR data in the N3C from 1 January 2018 to 7 April 2022. HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational aging-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated three types of pregnancy cohorts based on the level of precision for gestational aging and pregnancy outcomes for comparison of COVID-19 and other characteristics. Results: We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, spontaneous abortions), and 23.3% had unknown outcomes. We were able to estimate start dates within one week of precision for 431,173 (52.8%) episodes. 66,019 (8.1%) episodes had incident COVID-19 during pregnancy. Across varying COVID-19 cohorts, patient characteristics were generally similar though pregnancy outcomes differed. Discussion: HIPPS provides support for pregnancy-related variables based on EHR data for researchers to define pregnancy cohorts. Our approach performed well based on clinician validation. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational aging that addresses data inconsistency and missingness in EHR data.
Language	English
Publishing date	2022-08-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2022.08.04.22278439
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Article ; Online: NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study.

Virology journal

2022 Volume 19, Issue 1, Page(s) 84

Abstract: Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 ... ...

Abstract	Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. Methods: A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. Results: Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. Conclusions: Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.
MeSH term(s)	Acute Kidney Injury ; Anti-Inflammatory Agents, Non-Steroidal/adverse effects ; COVID-19 ; COVID-19 Testing ; Cohort Studies ; Humans ; Pandemics ; Retrospective Studies
Chemical Substances	Anti-Inflammatory Agents, Non-Steroidal
Language	English
Publishing date	2022-05-15
Publishing country	England
Document type	Journal Article ; Multicenter Study ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2160640-7
ISSN	1743-422X ; 1743-422X
ISSN (online)	1743-422X
ISSN	1743-422X
DOI	10.1186/s12985-022-01813-2
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