Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 7

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Alterations in the placental proteome in association with the presence of black carbon particles: A discovery study

Millen, Joline L., Luyten, Leen J., Dieu, Marc, Bové, Hannelore, Ameloot, Marcel, Bongaerts, Eva, Demazy, Catherine, Fransolet, Maude, Martens, Dries S., Renard, Patricia, Reimann, Brigitte, Plusquin, Michelle, Nawrot, Tim S., Debacq-Chainiaux, Florence·2024·Environmental Research
DOI 10.1016/j.envres.2024.120214
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.envres.2024.120214

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Research works printed but not linked: 67

  1. Bao, 2019, Transcriptome profiling revealed multiple genes and ECM-receptor interaction pathways that may be associated with breast cancer, Cell. Mol. Biol. Lett., 24, 38, 10.1186/s11658-019-0162-0 · DOI
  2. Bongaerts, 2022, Maternal exposure to ambient black carbon particles and their presence in maternal and fetal circulation and organs: an analysis of two independent population-based observational studies, Lancet Planet. Health, 6, e804, 10.1016/S2542-5196(22)00200-5 · DOI
  3. Bové, 2019, Ambient black carbon particles reach the fetal side of human placenta, Nat. Commun., 10, 3866, 10.1038/s41467-019-11654-3 · DOI · Paper page
  4. Chiu, 2010, A label-free differential proteomic analysis of mouse bronchoalveolar lavage fluid exposed to ultrafine carbon black, Anal. Chim. Acta, 673, 160, 10.1016/j.aca.2010.05.041 · DOI
  5. Clemente, 2016, Prenatal ambient air pollution, placental mitochondrial DNA content, and birth weight in the INMA (Spain) and ENVIRONAGE (Belgium) birth cohorts, Environ. Health Perspect., 124, 659, 10.1289/ehp.1408981 · DOI · Paper page
  6. Costa, 2006, Contractile cells and fibrillin-1 distribution is disturbed in terminal villi of placentae from patients with preeclampsia and systemic lupus erythematosus, Placenta, 27, 234, 10.1016/j.placenta.2005.02.018 · DOI
  7. Cox, 2017, The role of cellular senescence in ageing of the placenta, Placenta, 52, 139, 10.1016/j.placenta.2017.01.116 · DOI · Paper page
  8. Delforce, 2019, The Angiotensin II type 1 receptor mediates the effects of low oxygen on early placental angiogenesis, Placenta, 75, 54, 10.1016/j.placenta.2018.12.001 · DOI
  9. Distler, 2016, Label-free quantification in ion mobility-enhanced data-independent acquisition proteomics, Nat. Protoc., 11, 795, 10.1038/nprot.2016.042 · DOI
  10. Dodd-Butera, 2017, Placental biomarkers of PAH exposure and glutathione-S-transferase biotransformation enzymes in an obstetric population from Tijuana, Baja California, Mexico, Environ. Res., 152, 360, 10.1016/j.envres.2016.04.019 · DOI
  11. Fajersztajn, 2017, Hypoxia: from placental development to fetal programming, Birth Defects Res, 109, 1377, 10.1002/bdr2.1142 · DOI
  12. 2017, Expansion of the gene Ontology knowledgebase and resources, Nucleic Acids Res., 45, D331, 10.1093/nar/gkw1108 · DOI · Paper page
  13. Goriainova, 2022, Adverse effects of black carbon (BC) exposure during pregnancy on maternal and fetal health: a contemporary review, Toxics, 10, 10.3390/toxics10120779 · DOI
  14. Guo, 2021, Novel perspectives of environmental proteomics, Sci. Total Environ., 788, 10.1016/j.scitotenv.2021.147588 · DOI
  15. Hajat, 2015, Long-term exposure to air pollution and markers of inflammation, coagulation, and endothelial activation: a repeat-measures analysis in the Multi-Ethnic Study of Atherosclerosis (MESA), Epidemiology, 26, 310, 10.1097/EDE.0000000000000267 · DOI
  16. Hincal, 1986, Effects of exposure to air pollution and smoking on the placental aryl hydrocarbon hydroxylase (AHH) activity, Arch. Environ. Health, 41, 377, 10.1080/00039896.1986.9935782 · DOI
  17. Huuskonen, 2016, The human placental proteome is affected by maternal smoking, Reprod. Toxicol., 63, 22, 10.1016/j.reprotox.2016.05.009 · DOI
  18. Jacobs, 2010, Air pollution related prothrombotic changes in persons with diabetes, Environ. Health Perspect., 118, 191, 10.1289/ehp.0900942 · DOI
  19. Janssen, 2008, Spatial interpolation of air pollution measurements using CORINE land cover data, Atmos. Environ., 42, 4884, 10.1016/j.atmosenv.2008.02.043 · DOI
  20. Janssen, 2012, Placental mitochondrial DNA content and particulate air pollution during in utero life, Environ. Health Perspect., 120, 1346, 10.1289/ehp.1104458 · DOI
  21. Janssen, 2017, Cohort Profile: the ENVIRonmental influence ON early AGEing (ENVIRONAGE): a birth cohort study, Int. J. Epidemiol., 46, 1386, 10.1093/ije/dyx033 · DOI
  22. Kanehisa, 2017, KEGG: new perspectives on genomes, pathways, diseases and drugs, Nucleic Acids Res., 45, D353, 10.1093/nar/gkw1092 · DOI · Paper page
  23. Kerkhof, 2014, Transient early wheeze and lung function in early childhood associated with chronic obstructive pulmonary disease genes, J. Allergy Clin. Immunol., 133, 68, 10.1016/j.jaci.2013.06.004 · DOI
  24. Kingsley, 2017, Maternal residential air pollution and placental imprinted gene expression, Environ. Int., 108, 204, 10.1016/j.envint.2017.08.022 · DOI
  25. Korkes, 2017, Relationship between hypoxia and downstream pathogenic pathways in preeclampsia, Hypertens. Pregnancy, 36, 145, 10.1080/10641955.2016.1259627 · DOI
  26. Lager, 2012, Regulation of nutrient transport across the placenta, J Pregnancy, 2012, 10.1155/2012/179827 · DOI · Paper page
  27. Lefebvre, 2011, Validation of the MIMOSA-AURORA-IFDM model chain for policy support: modeling concentrations of elemental carbon in Flanders, Atmos. Environ., 45, 6705, 10.1016/j.atmosenv.2011.08.033 · DOI · Paper page
  28. Lefebvre, 2013, Presentation and evaluation of an integrated model chain to respond to traffic- and health-related policy questions, Environ. Model. Software, 40, 160, 10.1016/j.envsoft.2012.09.003 · DOI
  29. Li, 2018, Genomic analyses based on pulmonary adenocarcinoma in situ reveal early lung cancer signature, BMC Med Genomics, 11, 106, 10.1186/s12920-018-0413-3 · DOI
  30. Lin, 2013, A combinatorial approach to the peptide feature matching problem for label-free quantification, Bioinformatics, 29, 1768, 10.1093/bioinformatics/btt274 · DOI
  31. Liu, 2020, Oncogenic network and hub genes for natural killer/T-cell lymphoma utilizing WGCNA, Front. Oncol., 10, 223, 10.3389/fonc.2020.00223 · DOI
  32. Luyten, 2018, Air pollution and the fetal origin of disease: a systematic review of the molecular signatures of air pollution exposure in human placenta, Environ. Res., 166, 310, 10.1016/j.envres.2018.03.025 · DOI
  33. Luyten, 2020, Optimization of label-free nano LC-MS/MS analysis of the placental proteome, Placenta, 101, 159, 10.1016/j.placenta.2020.09.013 · DOI
  34. Magalhães, 2018, Comparison of urine and plasma peptidome indicates selectivity in renal peptide handling, Proteomics Clin Appl, 12, 10.1002/prca.201700163 · DOI
  35. Mandakh, 2020, Maternal exposure to ambient air pollution and risk of preeclampsia: a population-based cohort study in scania, Sweden, Int J Environ Res Public Health, 17, 10.3390/ijerph17051744 · DOI
  36. Martens, 2016, Air pollution stress and the aging phenotype: the telomere connection, Curr Environ Health Rep, 3, 258, 10.1007/s40572-016-0098-8 · DOI
  37. Martens, 2021, Urinary peptidomic profiles to address age-related disabilities: a prospective population study, Lancet Healthy Longev, 2, e690, 10.1016/S2666-7568(21)00226-9 · DOI
  38. Mathew, 2017, Hypoxia primed placental mesenchymal stem cells for wound healing, Life Sci., 182, 85, 10.1016/j.lfs.2017.06.016 · DOI
  39. Mayhew, 2003, Fibrin-type fibrinoid in placentae from pregnancies associated with maternal smoking: association with villous trophoblast and impact on intervillous porosity, Placenta, 24, 501, 10.1053/plac.2002.0943 · DOI
  40. Medeiros Tavares Marques, 2017, Identification of new genes associated to senescent and tumorigenic phenotypes in mesenchymal stem cells, Sci. Rep., 7, 10.1038/s41598-017-16224-5 · DOI
  41. Meier, 2018, Online parallel accumulation-serial fragmentation (PASEF) with a novel trapped ion mobility mass spectrometer, Mol. Cell. Proteomics, 17, 2534, 10.1074/mcp.TIR118.000900 · DOI · Paper page
  42. Neven, 2018, Placental promoter methylation of DNA repair genes and prenatal exposure to particulate air pollution: an ENVIRONAGE cohort study, Lancet Planet. Health, 2, e174, 10.1016/S2542-5196(18)30049-4 · DOI · Paper page
  43. Neven, 2021, Ambient air pollution exposure during the late gestational period is linked with lower placental iodine load in a Belgian birth cohort, Environ. Int., 147, 10.1016/j.envint.2020.106334 · DOI
  44. Obolenskaya, 2010, Human placental glutathione S-transferase activity and polycyclic aromatic hydrocarbon DNA adducts as biomarkers for environmental oxidative stress in placentas from pregnant women living in radioactivity- and chemically-polluted regions, Toxicol. Lett., 196, 80, 10.1016/j.toxlet.2010.03.1115 · DOI
  45. Pang, 2003, Expression profile of trophoblast invasion-associated genes in the pre-eclamptic placenta, Br. J. Biomed. Sci., 60, 97, 10.1080/09674845.2003.11783682 · DOI
  46. Pedersen, 2014, Ambient air pollution and pregnancy-induced hypertensive disorders: a systematic review and meta-analysis, Hypertension, 64, 494, 10.1161/HYPERTENSIONAHA.114.03545 · DOI
  47. Ramirez, 2012, Chronic and intermittent hypoxia differentially regulate left ventricular inflammatory and extracellular matrix responses, Hypertens. Res., 35, 811, 10.1038/hr.2012.32 · DOI
  48. Ruprecht, 2017, Environmental pollution and emission factors of electronic cigarettes, heat-not-burn tobacco products, and conventional cigarettes, Aerosol. Sci. Technol., 51, 674, 10.1080/02786826.2017.1300231 · DOI
  49. Saenen, 2016, Placental nitrosative stress and exposure to ambient air pollution during gestation: a population study, Am. J. Epidemiol., 184, 442, 10.1093/aje/kww007 · DOI · Paper page
  50. Saenen, 2017, Children's urinary environmental carbon load. A novel marker reflecting residential ambient air pollution exposure?, Am. J. Respir. Crit. Care Med., 196, 873, 10.1164/rccm.201704-0797OC · DOI · Paper page
  51. Sibley, 2002, Regulation of placental transfer: the Na(+)/H(+) exchanger--a review, Placenta, 23, S39, 10.1053/plac.2002.0790 · DOI
  52. Sohlberg, 2014, Placental perfusion in normal pregnancy and early and late preeclampsia: a magnetic resonance imaging study, Placenta, 35, 202, 10.1016/j.placenta.2014.01.008 · DOI
  53. Stylianopoulos, 2012, Causes, consequences, and remedies for growth-induced solid stress in murine and human tumors, Proc Natl Acad Sci U S A, 109, 15101, 10.1073/pnas.1213353109 · DOI · Paper page
  54. Szklarczyk, 2015, STRING v10: protein-protein interaction networks, integrated over the tree of life, Nucleic Acids Res., 43, D447, 10.1093/nar/gku1003 · DOI · Paper page
  55. Szklarczyk, 2019, STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res., 47, D607, 10.1093/nar/gky1131 · DOI · Paper page
  56. Veras, 2017, Before the first breath: prenatal exposures to air pollution and lung development, Cell Tissue Res., 367, 445, 10.1007/s00441-016-2509-4 · DOI
  57. Vos, 2021, Mitochondrial DNA methylation in placental tissue: a proof of concept study by means of prenatal environmental stressors, Epigenetics, 16, 121, 10.1080/15592294.2020.1790923 · DOI
  58. Vuong, 2016, Proteomic changes in human lung epithelial cells (A549) in response to carbon black and titanium dioxide exposures, J Proteomics, 149, 53, 10.1016/j.jprot.2016.03.046 · DOI
  59. Wang, 2013, Comparative proteome profile of human placenta from normal and preeclamptic pregnancies, PLoS One, 8
  60. Weidner, 2014, Aging of blood can be tracked by DNA methylation changes at just three CpG sites, Genome Biol., 15, R24, 10.1186/gb-2014-15-2-r24 · DOI · Paper page
  61. Wiśniewski, 2009, Universal sample preparation method for proteome analysis, Nat. Methods, 6, 359, 10.1038/nmeth.1322 · DOI · Paper page
  62. Yang, 2015, The proteomic analysis of human placenta with pre-eclampsia and normal pregnancy, J Korean Med Sci, 30, 770, 10.3346/jkms.2015.30.6.770 · DOI
  63. Yong, 2014, Increased decidual mRNA expression levels of candidate maternal pre-eclampsia susceptibility genes are associated with clinical severity, Placenta, 35, 117, 10.1016/j.placenta.2013.11.008 · DOI
  64. Yong, 2015, Anti-angiogenic collagen fragment arresten is increased from 16 weeks' gestation in pre-eclamptic plasma, Placenta, 36, 1300, 10.1016/j.placenta.2015.08.013 · DOI
  65. Yuen, 2010, DNA methylation profiling of human placentas reveals promoter hypomethylation of multiple genes in early-onset preeclampsia, Eur. J. Hum. Genet., 18, 1006, 10.1038/ejhg.2010.63 · DOI
  66. Zhang, 2012, PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification, Mol. Cell. Proteomics, 11, 10.1074/mcp.M111.010587 · DOI
  67. Zhang, 2020, Time- and dose-resolved proteome of PM(2.5)-exposure-induced lung injury and repair in rats, J. Proteome Res., 19, 3162, 10.1021/acs.jproteome.0c00155 · DOI

Cited by

Metadata

Authors
Millen, J. L., Luyten, L. J., Dieu, M., Bové, H., Ameloot, M., Bongaerts, E., Demazy, C., Fransolet, M., Martens, D. S., Renard, P., Reimann, B., Plusquin, M., Nawrot, T. S., & Debacq-Chainiaux, F.
Venue
Environmental Research
Publisher
Elsevier BV
Published
2024-12
Volume
263
Pages
120214
ISSN
0013-9351
DOI
10.1016/j.envres.2024.120214
Cited by
7
References
5
Language
en
Licence
Funders
  • European Research Council (ERC-2012-StG310898)
  • Fonds De La Recherche Scientifique - FNRS (12P6819N, 12\u00D79620N)
  • Fonds Wetenschappelijk Onderzoek (BOF21DOCNA03, BOF15DOCNA01, ERC-2012-StG310898, N1516112/G.0.873.11)
  • Universiteit Hasselt

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.