cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7665262
title: Altered nanoparticle uptake by lung carcinoma cells when stimulated with epidermal growth factor

description: This dataset provides the raw data supporting the paper "Altered nanoparticle uptake by lung carcinoma cells when stimulated with epidermal growth factor". The focus of the study was to investigate the uptake of two different sizes of silica NPs and gold NPs in lung epithelial cells A549 in the presence of epidermal growth factor (EGF). The data set includes: Screening for EGF receptor using western blot and confocal microscopy (Figure 1 and Figure S1) Investigating expression of RAC1/CDC42 proteins upon EGF stimulation using Western blot (Figure 2) Investigating expression of RAC1 gene upon EGF stimulation using RT-qPCR (Figure S2) Evaluating uptake of endocytic markers upon EGF stimulation using confocal laser scanning microscopy (Figure 3, Figure S4) and flow cytometry (Figure 3) Nanoparticle characterization using TEM (Figure 4, Figure S6) and UV-Vis (Figure S5, Figure S6) Evaluating silica nanoparticle uptake upon EGF stimulation using confocal laser scanning microscopy (Figure 5, Figure S8) and flow cytometry (Figure 5) Evaluating gold nanoparticle uptake upon EGF stimulation using dark-field microscopy and ICP-AES (Figure 6) Investigating expression of c-MYC gene upon EGF stimulation using RT-qPCR (Figure 6) Cell viability results, analysed via lactate dehydrogenase assay (Figure S3) and MTS assay (Figure S9) Raw integrated density data from dark-field images (Figure S9)
DOI: 10.5281/zenodo.7665261
title: Altered nanoparticle uptake by lung carcinoma cells when stimulated with epidermal growth factor

description: This dataset provides the raw data supporting the paper "Altered nanoparticle uptake by lung carcinoma cells when stimulated with epidermal growth factor". The focus of the study was to investigate the uptake of two different sizes of silica NPs and gold NPs in lung epithelial cells A549 in the presence of epidermal growth factor (EGF). The data set includes: Screening for EGF receptor using western blot and confocal microscopy (Figure 1 and Figure S1) Investigating expression of RAC1/CDC42 proteins upon EGF stimulation using Western blot (Figure 2) Investigating expression of RAC1 gene upon EGF stimulation using RT-qPCR (Figure S2) Evaluating uptake of endocytic markers upon EGF stimulation using confocal laser scanning microscopy (Figure 3, Figure S4) and flow cytometry (Figure 3) Nanoparticle characterization using TEM (Figure 4, Figure S6) and UV-Vis (Figure S5, Figure S6) Evaluating silica nanoparticle uptake upon EGF stimulation using confocal laser scanning microscopy (Figure 5, Figure S8) and flow cytometry (Figure 5) Evaluating gold nanoparticle uptake upon EGF stimulation using dark-field microscopy and ICP-AES (Figure 6) Investigating expression of c-MYC gene upon EGF stimulation using RT-qPCR (Figure 6) Cell viability results, analysed via lactate dehydrogenase assay (Figure S3) and MTS assay (Figure S9) Raw integrated density data from dark-field images (Figure S9)
DOI: 10.25934/pr00008811
title: AMR and age

description: Proportion of infections that are resistance to infections differ by age and gender. Understanding these differences can help inform public health practice by tailoring prescription guidelines by age and gender. This may lead to improved patient outcomes and a reduction in inappropiate prescriptions, strengthening antibiotics stewardship. There are indiciations that the trends differ by bacteria-antibiotic combination, and we will therefore analyse these trends in subsets.
DOI: 10.23645/epacomptox.22149200
title: CCTE Mundy High-Content Imaging Neurite Outgrowth Data Update

description: This data contains the results of curve-fitting a subset of high-content imaging neurite outgrowth assay data generated by the Mundy Lab at the EPA Center for Computational Toxicology. Data was curve-fit with tcplLite using tcpl version 2.1.0. Data from the CCTE_Mundy_HCI_iCellGluta_NOG assay corresponds to a correction of the CCTE_Mundy_HCI_CDI_NOG assay data in invitroDBv3.5 for 8 test samples. These corrections will be reflected in a future release of invitroDB. Data from the CCTE_Mundy_HCI_iCellGABA_NOG assay corresponds to 27 test samples that are not present in invitroDBv3.5. This data will be included in a future release of invitroDB. <br> Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25
DOI: 10.6084/m9.figshare.19290199
title: A scoping review of asthma and machine learning

description: The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature. We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020. DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented. A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status). The use of ML in asthma studies has been increasing with most of this focused on the three major topics (&gt;50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence). Supplemental data for this article is available online at http://dx.doi.org/ .
DOI: 10.6084/m9.figshare.19290199.v2
title: A scoping review of asthma and machine learning

description: The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature. We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020. DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented. A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status). The use of ML in asthma studies has been increasing with most of this focused on the three major topics (&gt;50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence). Supplemental data for this article is available online at http://dx.doi.org/ .
DOI: 10.6084/m9.figshare.19354987
title: Cost analysis of hospitals performing continuous albuterol in non-intensive care settings

description: To compare hospital costs and resource utilization for pediatric asthma admissions based on the hospitals’ availability of continuous albuterol aerosolization administration (CAA) in non-intensive care unit (ICU) settings. We conducted a retrospective cohort study of children ages 2-17 years admitted in 2019 with a principal diagnosis of asthma using the Pediatric Health Information System. Hospitals and hospitalizations were categorized based on location of CAA administration, ICU-only versus general inpatient floors. Hospitals preforming CAA in an intermediate care unit were excluded. We calculated total cost, standardized unit costs and rates of interventions. Groups were compared using Chi-Square, t-test and Wilcoxon rank-sum test as indicated. A log linear mixed model was created to evaluate potential confounders. Twenty-one hospitals (7084 hospitalizations) allowed CAA on the floor. Twenty-four hospitals (6100 hospitalizations) allowed CAA in the ICU-only. Median total cost was $4639 (Interquartile Range (IQR) $3060–$7512) for the floor group and $5478 (IQR $3444–$8539) for the ICU-only group (<i>p</i> &lt; 0.001) (mean cost difference of $775 per patient). Hospitalization costs were $4,726,829 (95% CI $3,459,920–$5,993,860) greater for the children treated at hospitals restricting CAA to the ICU. We observed higher standardized laboratory, imaging, clinical and other unit costs, along with higher use of interventions in the ICU-only group. After adjustment, we found that ICU stay and hospital LOS were the main drivers of cost difference between the groups. There was cost savings and decreased resource utilization for hospitals that performed CAA on the floor. Further studies exploring variations in asthma management are warranted.
DOI: 10.6084/m9.figshare.19354987.v1
title: Cost analysis of hospitals performing continuous albuterol in non-intensive care settings

description: To compare hospital costs and resource utilization for pediatric asthma admissions based on the hospitals’ availability of continuous albuterol aerosolization administration (CAA) in non-intensive care unit (ICU) settings. We conducted a retrospective cohort study of children ages 2-17 years admitted in 2019 with a principal diagnosis of asthma using the Pediatric Health Information System. Hospitals and hospitalizations were categorized based on location of CAA administration, ICU-only versus general inpatient floors. Hospitals preforming CAA in an intermediate care unit were excluded. We calculated total cost, standardized unit costs and rates of interventions. Groups were compared using Chi-Square, t-test and Wilcoxon rank-sum test as indicated. A log linear mixed model was created to evaluate potential confounders. Twenty-one hospitals (7084 hospitalizations) allowed CAA on the floor. Twenty-four hospitals (6100 hospitalizations) allowed CAA in the ICU-only. Median total cost was $4639 (Interquartile Range (IQR) $3060–$7512) for the floor group and $5478 (IQR $3444–$8539) for the ICU-only group (<i>p</i> &lt; 0.001) (mean cost difference of $775 per patient). Hospitalization costs were $4,726,829 (95% CI $3,459,920–$5,993,860) greater for the children treated at hospitals restricting CAA to the ICU. We observed higher standardized laboratory, imaging, clinical and other unit costs, along with higher use of interventions in the ICU-only group. After adjustment, we found that ICU stay and hospital LOS were the main drivers of cost difference between the groups. There was cost savings and decreased resource utilization for hospitals that performed CAA on the floor. Further studies exploring variations in asthma management are warranted.
DOI: 10.6084/m9.figshare.20141429
title: Preferences for support in managing symptoms of an asthma flare-up: a pilot study of a discrete choice experiment

description: <b>Objective:</b> Information on the preferences of people with asthma for support in managing a flare-up can inform service design which may facilitate appropriate help-seeking. To date, little is known about support preferences for managing a flare-up. The aim of this study was to develop and pilot a discrete choice experiment (DCE) to elicit the preferences of people with asthma with regards to support in managing a flare-up. <b>Methods:</b> Steps in developing the DCE included identification and selection of attributes and levels of the support services, construction of choice tasks, experimental design, construction of DCE instrument, and pretest (n=16) and pilot (n=38) studies of the DCE instrument. A multinomial logit model was used to examine the strength and direction of the six attributes in the pilot study. <b>Results:</b> Our results indicate that from a patient perspective, having a healthcare professional that listens to their concerns was the most valued attribute of support in asthma flare-up management. The other features of support valued by participants were timely access to consultation, a healthcare professional with knowledge of their patient history, a specialist doctor and face-to-face communication. Having a written action plan was the least valued attribute. <b>Conclusions:</b> Our findings suggest patient preference for a model of support in managing their symptoms which includes timely, face-to-face access to a healthcare professional that knows them and listens to their concerns. The findings of the pilot study need to be verified with a larger sample and using models to account for preference heterogeneity.
DOI: 10.6084/m9.figshare.20141429.v1
title: Preferences for support in managing symptoms of an asthma flare-up: a pilot study of a discrete choice experiment

description: <b>Objective:</b> Information on the preferences of people with asthma for support in managing a flare-up can inform service design which may facilitate appropriate help-seeking. To date, little is known about support preferences for managing a flare-up. The aim of this study was to develop and pilot a discrete choice experiment (DCE) to elicit the preferences of people with asthma with regards to support in managing a flare-up. <b>Methods:</b> Steps in developing the DCE included identification and selection of attributes and levels of the support services, construction of choice tasks, experimental design, construction of DCE instrument, and pretest (n=16) and pilot (n=38) studies of the DCE instrument. A multinomial logit model was used to examine the strength and direction of the six attributes in the pilot study. <b>Results:</b> Our results indicate that from a patient perspective, having a healthcare professional that listens to their concerns was the most valued attribute of support in asthma flare-up management. The other features of support valued by participants were timely access to consultation, a healthcare professional with knowledge of their patient history, a specialist doctor and face-to-face communication. Having a written action plan was the least valued attribute. <b>Conclusions:</b> Our findings suggest patient preference for a model of support in managing their symptoms which includes timely, face-to-face access to a healthcare professional that knows them and listens to their concerns. The findings of the pilot study need to be verified with a larger sample and using models to account for preference heterogeneity.
DOI: 10.6084/m9.figshare.19390559
title: Identification of hub genes and potential biomarkers of neutrophilic asthma: evidence from a bioinformatics analysis

description: Asthma is a chronic airway inflammatory disease caused by multiple genetic and environmental factors. This study mainly sought to provide potential therapeutic targets and biomarkers for neutrophilic asthma (NA). Three gene expression profiling datasets were obtained from the Genome Expression Omnibus (GEO) database. GSE45111 and GSE41863 were used to identify hub genes and potential biomarkers, and GSE137268 was used for data verification. We verified the repeatability of intragroup data and identified differentially expressed genes (DEGs). Then, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs, and a protein–protein interaction (PPI) network was constructed to identify the hub genes. Finally, receiver operating characteristic (ROC) analysis was used to verify the ability of the hub genes to differentiate between NA and eosinophilic asthma (EA). In this study, we identified 411 DEGs by comprehensive analysis of NA/EA patients and NA/healthy controls (HCs). Ten hub genes (CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, IL6R, JAK3, CD48, and GNG2) were identified from the PPI network. Finally, based on the ROC analysis, 7 genes showed good diagnostic value for discriminating NA from EA—CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, and IL6R (AUC &gt; 0.7). We identified 7 hub genes that can distinguish NA from EA. The IL-8-mediated signaling may be the primary pathway to determine the NA phenotype in asthma. CXCR1/2 and S100A12 may be the primary genes determining the NA phenotype. CXCR1/2 and S100A12 might be biomarkers and new therapeutic targets for NA. Supplemental data for this article is available online at at
DOI: 10.6084/m9.figshare.19390559.v2
title: Identification of hub genes and potential biomarkers of neutrophilic asthma: evidence from a bioinformatics analysis

description: Asthma is a chronic airway inflammatory disease caused by multiple genetic and environmental factors. This study mainly sought to provide potential therapeutic targets and biomarkers for neutrophilic asthma (NA). Three gene expression profiling datasets were obtained from the Genome Expression Omnibus (GEO) database. GSE45111 and GSE41863 were used to identify hub genes and potential biomarkers, and GSE137268 was used for data verification. We verified the repeatability of intragroup data and identified differentially expressed genes (DEGs). Then, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs, and a protein–protein interaction (PPI) network was constructed to identify the hub genes. Finally, receiver operating characteristic (ROC) analysis was used to verify the ability of the hub genes to differentiate between NA and eosinophilic asthma (EA). In this study, we identified 411 DEGs by comprehensive analysis of NA/EA patients and NA/healthy controls (HCs). Ten hub genes (CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, IL6R, JAK3, CD48, and GNG2) were identified from the PPI network. Finally, based on the ROC analysis, 7 genes showed good diagnostic value for discriminating NA from EA—CXCR1, FCGR3B, CXCR2, SELL, S100A12, CSF3R, and IL6R (AUC &gt; 0.7). We identified 7 hub genes that can distinguish NA from EA. The IL-8-mediated signaling may be the primary pathway to determine the NA phenotype in asthma. CXCR1/2 and S100A12 may be the primary genes determining the NA phenotype. CXCR1/2 and S100A12 might be biomarkers and new therapeutic targets for NA. Supplemental data for this article is available online at at
DOI: 10.5281/zenodo.7456922
title: smFISH Analysis Source code

description: Source code of smFISH image analysis scripts (MATLAB) ; List of smiFISH probes used in this work.
DOI: 10.5281/zenodo.7674721
title: smFISH Analysis Source code

description: Source code of smFISH image analysis scripts (MATLAB) ; List of smiFISH probes used in this work.
DOI: 10.6084/m9.figshare.22154822
title: Mechanical properties

description: Mechanical properties
DOI: 10.5281/zenodo.7641621
title: Metabolomics data: glutaminase inhibition in combination with azacytidine in myelodysplastic syndromes

description: Malignancies can become reliant on glutamine as an alternative energy source and as a facilitator of aberrant DNA methylation, thus implicating glutaminase (GLS) as a potential therapeutic target. We demonstrate preclinical synergy of Telaglenastat (CB-839), a selective GLS inhibitor, when combined with AZA, <em>in vitro</em> and <em>in vivo</em>, followed by a phase Ib/II study of the combination in patients with advanced MDS. Treatment with Telaglenastat/AZA led to an ORR of 70% with CRs in 66% patients and a median overall survival of 11.6 months. scRNAseq and flow cytometry demonstrated a myeloid differentiation program at the stem cell level in clinical responders. Expression of non-canonical glutamine transporter, SLC38A1, was found to be overexpressed in MDS stem cells and was associated with clinical responses to Telaglenastat/AZA; was predictive of worse prognosis in a large MDS cohort. These data demonstrate the safety and efficacy of a combined metabolic and epigenetic approach in MDS.
DOI: 10.5281/zenodo.6442444
title: Metabolic data: Inhibition of mitochondrial complex I reverses NOTCH1-driven metabolic reprogramming in T-cell acute lymphoblastic leukemia

description: T-cell acute lymphoblastic leukemia (T-ALL) is commonly driven by activating mutations in <em>NOTCH1 </em>that facilitate glutamine oxidation. Here we identify oxidative phosphorylation (OxPhos) as a critical pathway for leukemia cell survival and demonstrate a direct relationship between <em>NOTCH1</em>, elevated OxPhos gene expression, and acquired chemoresistance in pre-leukemic and leukemic models. Disrupting OxPhos with IACS-010759, an inhibitor of mitochondrial complex I, causes potent growth inhibition through induction of metabolic shut-down and redox imbalance in <em>NOTCH1</em>-mutated and less so in <em>NOTCH1</em>-wt T-ALL cells. Mechanistically, inhibition of OxPhos induces metabolic reprogramming into glutaminolysis. We show that pharmacological blockade of OxPhos combined with inducible knock-down of glutaminase, the key glutamine enzyme, confers synthetic lethality in mice harboring <em>NOTCH1</em>-mutated T-ALL<em>. </em>We leverage this synthetic lethal interaction to demonstrate that IACS-010759 in combination with chemotherapy containing L-asparaginase, an enzyme that uncovers the glutamine dependency of leukemic cells, causes reduced glutaminolysis and profound tumor reduction in pre-clinical models of human T-ALL. In summary, this metabolic dependency of T-ALL on OxPhos provides a rational therapeutic target.
DOI: 10.5281/zenodo.6442443
title: Metabolic data: Inhibition of mitochondrial complex I reverses NOTCH1-driven metabolic reprogramming in T-cell acute lymphoblastic leukemia

description: T-cell acute lymphoblastic leukemia (T-ALL) is commonly driven by activating mutations in <em>NOTCH1 </em>that facilitate glutamine oxidation. Here we identify oxidative phosphorylation (OxPhos) as a critical pathway for leukemia cell survival and demonstrate a direct relationship between <em>NOTCH1</em>, elevated OxPhos gene expression, and acquired chemoresistance in pre-leukemic and leukemic models. Disrupting OxPhos with IACS-010759, an inhibitor of mitochondrial complex I, causes potent growth inhibition through induction of metabolic shut-down and redox imbalance in <em>NOTCH1</em>-mutated and less so in <em>NOTCH1</em>-wt T-ALL cells. Mechanistically, inhibition of OxPhos induces metabolic reprogramming into glutaminolysis. We show that pharmacological blockade of OxPhos combined with inducible knock-down of glutaminase, the key glutamine enzyme, confers synthetic lethality in mice harboring <em>NOTCH1</em>-mutated T-ALL<em>. </em>We leverage this synthetic lethal interaction to demonstrate that IACS-010759 in combination with chemotherapy containing L-asparaginase, an enzyme that uncovers the glutamine dependency of leukemic cells, causes reduced glutaminolysis and profound tumor reduction in pre-clinical models of human T-ALL. In summary, this metabolic dependency of T-ALL on OxPhos provides a rational therapeutic target.