Cited by 57

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

Altered Extracellular Vesicle miRNA Profile in Prodromal Alzheimer’s Disease

Visconte, Caterina, Fenoglio, Chiara, Serpente, Maria, Muti, Paola, Sacconi, Andrea, Rigoni, Marta, Arighi, Andrea, Borracci, Vittoria, Arcaro, Marina, Arosio, Beatrice, Ferri, Evelyn, Golia, Maria Teresa, Scarpini, Elio, Galimberti, Daniela·2023·International Journal of Molecular Sciences

Abstract

Extracellular vesicles (EVs) are nanosized vesicles released by almost all body tissues, representing important mediators of cellular communication, and are thus promising candidate biomarkers for neurodegenerative diseases like Alzheimer’s disease (AD). The aim of the present study was to isolate total EVs from plasma and characterize their microRNA (miRNA) contents in AD patients. We isolated total EVs from the plasma of all recruited subjects using ExoQuickULTRA exosome precipitation solution (SBI). Subsequently, circulating total EVs were characterized using Nanosight nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and Western blotting. A panel of 754 miRNAs was determined with RT-qPCR using TaqMan OpenArray technology in a QuantStudio 12K System (Thermo Fisher Scientific). The results demonstrated that plasma EVs showed widespread deregulation of specific miRNAs (miR-106a-5p, miR-16-5p, miR-17-5p, miR-195-5p, miR-19b-3p, miR-20a-5p, miR-223-3p, miR-25-3p, miR-296-5p, miR-30b-5p, miR-532-3p, miR-92a-3p, and miR-451a), some of which were already known to be associated with neurological pathologies. A further validation analysis also confirmed a significant upregulation of miR-16-5p, miR-25-3p, miR-92a-3p, and miR-451a in prodromal AD patients, suggesting these dysregulated miRNAs are involved in the early progression of AD.

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 · 10.3390/ijms241914749

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Characterization of isolated plasma vesicles. (a) Representative trace of nanoparticle tracking analysis (NTA) of total EVs isolated using ExoQuick ULTRA. (b) Plasma, total EVs, and depleted EVs (DEVs) were analyzed by immunoblotting with antibodies against CD81, TGS101, and GM130, as indicated on the right. (c) TEM images revealed the peculiar oval shape of circulating EVs.
Figure 1 Characterization of isolated plasma vesicles. (a) Representative trace of nanoparticle tracking analysis (NTA) of total EVs isolated using ExoQuick ULTRA. (b) Plasma, total EVs, and depleted EVs (DEVs) were analyzed by immunoblotting with antibodies against CD81, TGS101, and GM130, as indicated on the right. (c) TEM images revealed the peculiar oval shape of circulating EVs.CC BY · PubMed Central
Figure 2 MiRNA expression profile in total plasma EVs. (a) Concentration of EV-derived miRNA after using miRNeasy Isolation Kit assessed by Bioanalyzer. (b) Pie chart showing deregulated miRNAs from the total number of miRNAs analyzed. (c) Box plots showing 2−ΔCt. and min and max values for CTRL, non-AD MCI, and AD patients. * p < 0.05; ** p < 0.005.
Figure 2 MiRNA expression profile in total plasma EVs. (a) Concentration of EV-derived miRNA after using miRNeasy Isolation Kit assessed by Bioanalyzer. (b) Pie chart showing deregulated miRNAs from the total number of miRNAs analyzed. (c) Box plots showing 2−ΔCt. and min and max values for CTRL, non-AD MCI, and AD patients. * p < 0.05; ** p < 0.005.CC BY · PubMed Central
Figure 3 miRNA expression levels in non-AD MCI, prodromal AD, AD, and CTRL, shown as 2−ΔCt. * p < 0.05; ** p < 0.005.
Figure 3 miRNA expression levels in non-AD MCI, prodromal AD, AD, and CTRL, shown as 2−ΔCt. * p < 0.05; ** p < 0.005.CC BY · PubMed Central
Figure 4 (a) PCA based on the most variant miRNAs in the discovery cohort (p = 0.004). (b) PCA based on the most variant miRNAs in the validation cohort (p = 0.007). The p-value between AD and control samples was computed using a Wilcoxon test applied to the first principal component.
Figure 4 (a) PCA based on the most variant miRNAs in the discovery cohort (p = 0.004). (b) PCA based on the most variant miRNAs in the validation cohort (p = 0.007). The p-value between AD and control samples was computed using a Wilcoxon test applied to the first principal component.CC BY · PubMed Central
Figure 5 (a) The Spearman correlation analysis between miR-30b-5p and Aβ42. (b) The Spearman correlation analysis between miR-30b-5p and Aβ42.
Figure 5 (a) The Spearman correlation analysis between miR-30b-5p and Aβ42. (b) The Spearman correlation analysis between miR-30b-5p and Aβ42.CC BY · PubMed Central
Figure 6 Network visualization for the enrichment analysis of miR-92a-3p, miR-16-5p, miR-25-3p, and miR-451a in prodromal AD and AD groups. Red/yellow squares represent deregulated miRNAs and pink circles are their target genes, while yellow circles are their lncRNA targets. Big red/yellow circles represent shared interactions.
Figure 6 Network visualization for the enrichment analysis of miR-92a-3p, miR-16-5p, miR-25-3p, and miR-451a in prodromal AD and AD groups. Red/yellow squares represent deregulated miRNAs and pink circles are their target genes, while yellow circles are their lncRNA targets. Big red/yellow circles represent shared interactions.CC BY · PubMed Central
Figure 7 MiRNA target and pathway prediction. (a) Biological pathway (y-axis) associated with mir-25-3p, mir-92a-3p, mir-16-5p and mir-451a signature. The significance of this association is expressed with –log10 (p-value) and is reported on x-axis. (b) Network visualization for the enrichment analysis of miR-92a-3p, miR-16-5p, miR-25-3p, and miR-451a in prodromal AD and AD group. Pink circles are their targets genes while yellow circles are their lncRNA targets. Big orange circles represent Wnt Signaling pathway; big blue circles show genes involved in phagosomes; red circles show gap-junction-associated genes; green circles represent genes related to synaptic vesicle cycle.
Figure 7 MiRNA target and pathway prediction. (a) Biological pathway (y-axis) associated with mir-25-3p, mir-92a-3p, mir-16-5p and mir-451a signature. The significance of this association is expressed with –log10 (p-value) and is reported on x-axis. (b) Network visualization for the enrichment analysis of miR-92a-3p, miR-16-5p, miR-25-3p, and miR-451a in prodromal AD and AD group. Pink circles are their targets genes while yellow circles are their lncRNA targets. Big orange circles represent Wnt Signaling pathway; big blue circles show genes involved in phagosomes; red circles show gap-junction-associated genes; green circles represent genes related to synaptic vesicle cycle.CC BY · PubMed Central

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

Research works printed but not linked: 66

  1. Liss, 2021, Practical recommendations for timely, accurate diagnosis of symptomatic Alzheimer’s disease (MCI and dementia) in primary care: A review and synthesis, J. Intern. Med., 290, 310, 10.1111/joim.13244 · DOI · Paper page
  2. Blennow, 2017, A Review of Fluid Biomarkers for Alzheimer’s Disease: Moving from CSF to Blood, Neurol. Ther., 6, 15, 10.1007/s40120-017-0073-9 · DOI · Paper page
  3. Aharon, 2020, Extracellular Vesicles of Alzheimer’s Disease Patients as a Biomarker for Disease Progression, Mol. Neurobiol., 57, 4156, 10.1007/s12035-020-02013-1 · DOI · Paper page
  4. Aulston, 2019, Extracellular Vesicles Isolated from Familial Alzheimer’s Disease Neuronal Cultures Induce Aberrant Tau Phosphorylation in the Wild-Type Mouse Brain, J. Alzheimer’s Dis. JAD, 72, 575, 10.3233/JAD-190656 · DOI
  5. Duggan, 2022, Exosomes in Age-Related Cognitive Decline: Mechanistic Insights and Improving Outcomes, Front. Aging Neurosci., 14, 834775, 10.3389/fnagi.2022.834775 · DOI
  6. Xiao, 2017, The Role of Exosomes in the Pathogenesis of Alzheimer’ Disease, Transl. Neurodegener., 6, 3, 10.1186/s40035-017-0072-x · DOI
  7. Pathan, 2019, Vesiclepedia 2019: A Compendium of RNA, Proteins, Lipids and Metabolites in Extracellular Vesicles, Nucleic Acids Res., 47, D516, 10.1093/nar/gky1029 · DOI
  8. Kalluri, 2020, The Biology, Function, and Biomedical Applications of Exosomes, Science, 367, eaau6977, 10.1126/science.aau6977 · DOI · Paper page
  9. Witwer, 2018, Minimal Information for Studies of Extracellular Vesicles 2018 (MISEV2018): A Position Statement of the International Society for Extracellular Vesicles and Update of the MISEV2014 Guidelines, J. Extracell. Vesicles, 7, 1535750, 10.1080/20013078.2018.1535750 · DOI · Paper page
  10. Herschlik, 2015, Emerging Roles of Exosomes in Normal and Pathological Conditions: New Insights for Diagnosis and Therapeutic Applications, Front. Immunol., 6, 203
  11. Kim, 2017, RNA in Extracellular Vesicles, Wiley Interdiscip. Rev. RNA, 8, e1413, 10.1002/wrna.1413 · DOI
  12. Abels, 2016, Introduction to Extracellular Vesicles: Biogenesis, RNA Cargo Selection, Content, Release, and Uptake, Cell. Mol. Neurobiol., 36, 301, 10.1007/s10571-016-0366-z · DOI
  13. Riva, 2016, The Long Non-Coding RNAs in Neurodegenerative Diseases: Novel Mechanisms of Pathogenesis, Curr. Alzheimer Res., 13, 1219, 10.2174/1567205013666160622112234 · DOI
  14. Kumar, 2016, Are Circulating microRNAs Peripheral Biomarkers for Alzheimer’s Disease?, Biochim. Biophys. Acta (BBA) Mol. Basis Dis., 1862, 1617, 10.1016/j.bbadis.2016.06.001 · DOI
  15. Campdelacreu, 2019, Exploratory Study on microRNA Profiles from Plasma-Derived Extracellular Vesicles in Alzheimer’s Disease and Dementia with Lewy Bodies, Transl. Neurodegener., 8, 1
  16. Sproviero, D., Gagliardi, S., Zucca, S., Arigoni, M., Giannini, M., Garofalo, M., Olivero, M., Dell’Orco, M., Pansarasa, O., and Bernuzzi, S. (2021). Different miRNA Profiles in Plasma Derived Small and Large Extracellular Vesicles from Patients with Neurodegenerative Diseases. Int. J. Mol. Sci., 22. · DOI
  17. Martinez, 2019, MicroRNAs as Diagnostic and Therapeutic Tools for Alzheimer’s Disease: Advances and Limitations, Neural Regen. Res., 14, 242, 10.4103/1673-5374.244784 · DOI
  18. Serpente, M., Fenoglio, C., D’Anca, M., Arcaro, M., Sorrentino, F., Visconte, C., Arighi, A., Fumagalli, G.G., Porretti, L., and Cattaneo, A. (2020). MiRNA Profiling in Plasma Neural-Derived Small Extracellular Vesicles from Patients with Alzheimer’s Disease. Cells, 9. · DOI · Paper page
  19. Hulse, 2013, Genetic Variants Contribute to Gene Expression Variability in Humans, Genetics, 193, 95, 10.1534/genetics.112.146779 · DOI
  20. Lukiw, 2013, Variability in Micro RNA (miRNA) Abundance, Speciation and Complexity amongst Different Human Populations and Potential Relevance to Alzheimer’s Disease (AD), Front. Cell Neurosci., 7, 133, 10.3389/fncel.2013.00133 · DOI
  21. Visconte, 2023, Plasma Microglial-Derived Extracellular Vesicles Are Increased in Frail Patients with Mild Cognitive Impairment and Exert a Neurotoxic Effect, Geroscience, 45, 1557, 10.1007/s11357-023-00746-0 · DOI
  22. Mogilyansky, 2013, The miR-17/92 Cluster: A Comprehensive Update on Its Genomics, Genetics, Functions and Increasingly Important and Numerous Roles in Health and Disease, Cell Death Differ., 20, 1603, 10.1038/cdd.2013.125 · DOI · Paper page
  23. Khuu, 2016, The Three Paralogous MicroRNA Clusters in Development and Disease, miR-17-92, miR-106a-363, and miR-106b-25, Scientifica, 2016, 1379643, 10.1155/2016/1379643 · DOI
  24. Mendell, 2008, miRiad Roles for the miR-17-92 Cluster in Development and Disease, Cell, 133, 217, 10.1016/j.cell.2008.04.001 · DOI
  25. McKeever, 2018, MicroRNA Expression Levels Are Altered in the Cerebrospinal Fluid of Patients with Young-Onset Alzheimer’s Disease, Mol. Neurobiol., 55, 8826, 10.1007/s12035-018-1032-x · DOI · Paper page
  26. Riancho, 2017, MicroRNA Profile in Patients with Alzheimer’s Disease: Analysis of miR-9-5p and miR-598 in Raw and Exosome Enriched Cerebrospinal Fluid Samples, J. Alzheimer’s Dis. JAD, 57, 483, 10.3233/JAD-161179 · DOI · Paper page
  27. Liu, 2014, MicroRNA-193b Is a Regulator of Amyloid Precursor Protein in the Blood and Cerebrospinal Fluid Derived Exosomal microRNA-193b Is a Biomarker of Alzheimer’s Disease, Mol. Med. Rep., 10, 2395, 10.3892/mmr.2014.2484 · DOI · Paper page
  28. Trindade, 2020, Diagnostic and Therapeutic Potential of Exosomes in Alzheimer’s Disease, J. Neurochem., 156, 162
  29. Cheng, 2015, Prognostic Serum miRNA Biomarkers Associated with Alzheimer’s Disease Shows Concordance with Neuropsychological and Neuroimaging Assessment, Mol. Psychiatry, 20, 1188, 10.1038/mp.2014.127 · DOI · Paper page
  30. Cheng, L., Sharples, R.A., Scicluna, B.J., and Hill, A.F. (2014). Exosomes Provide a Protective and Enriched Source of miRNA for Biomarker Profiling Compared to Intracellular and Cell-Free Blood. J. Extracell. Vesicles, 3. · DOI · Paper page
  31. Cheng, 2020, Small RNA Fingerprinting of Alzheimer’s Disease Frontal Cortex Extracellular Vesiclesand Their Comparison with Peripheral Extracellular Vesicles, J. Extracell. Vesicles, 9, 1766822, 10.1080/20013078.2020.1766822 · DOI
  32. Lugli, G., Cohen, A.M., Bennett, D.A., Shah, R.C., Fields, C.J., Hernandez, A.G., and Smalheiser, N.R. (2015). Plasma Exosomal miRNAs in Persons with and without Alzheimer Disease: Altered Expression and Prospects for Biomarkers. PLoS ONE, 10. · DOI · Paper page
  33. Nie, 2020, Differential Expression of Plasma Exo-miRNA in Neurodegenerative Diseases by Next-Generation Sequencing, Front. Neurosci., 14, 438, 10.3389/fnins.2020.00438 · DOI · Paper page
  34. Li, 2020, Profile of Pathogenic Proteins and MicroRNAs in Plasma-Derived Extracellular Vesicles in Alzheimer’s Disease: A Pilot Study, Neuroscience, 432, 240, 10.1016/j.neuroscience.2020.02.044 · DOI
  35. Bleazard, 2015, Bias in microRNA Functional Enrichment Analysis, Bioinformatics, 31, 1592, 10.1093/bioinformatics/btv023 · DOI
  36. Friedman, 2009, Most Mammalian mRNAs Are Conserved Targets of microRNAs, Genome Res., 19, 92, 10.1101/gr.082701.108 · DOI · Paper page
  37. Tanzer, 2004, Molecular Evolution of a microRNA Cluster, J. Mol. Biol., 339, 327, 10.1016/j.jmb.2004.03.065 · DOI
  38. Ventura, 2008, Targeted Deletion Reveals Essential and Overlapping Functions of the miR-17 through 92 Family of miRNA Clusters, Cell, 132, 875, 10.1016/j.cell.2008.02.019 · DOI
  39. Mestdagh, 2010, The miR-17-92 microRNA Cluster Regulates Multiple Components of the TGF-β Pathway in Neuroblastoma, Mol. Cell, 40, 762, 10.1016/j.molcel.2010.11.038 · DOI · Paper page
  40. Jin, 2022, Circular RNA Rbms1 Inhibited the Development of Myocardial Ischemia Reperfusion Injury by Regulating miR-92a/BCL2L11 Signaling Pathway, Bioengineered, 13, 3082, 10.1080/21655979.2022.2025696 · DOI
  41. Xiu, 2022, MicroRNA-17-5p Protects against Propofol Anesthesia-Induced Neurotoxicity and Autophagy Impairment via Targeting BCL2L11, Comput. Math. Methods Med., 2022, 6018037, 10.1155/2022/6018037 · DOI
  42. Petrocca, 2008, Emerging Role of miR-106b-25/miR-17-92 Clusters in the Control of Transforming Growth Factor Beta Signaling, Cancer Res., 68, 8191, 10.1158/0008-5472.CAN-08-1768 · DOI
  43. Bergmans, 2009, MicroRNA Regulation of Alzheimer’s Amyloid Precursor Protein Expression, Neurobiol. Dis., 33, 422, 10.1016/j.nbd.2008.11.009 · DOI · Paper page
  44. Rani, 2017, miRNA in Circulating Microvesicles as Biomarkers for Age-Related Cognitive Decline, Front. Aging Neurosci., 9, 323, 10.3389/fnagi.2017.00323 · DOI
  45. Grillari, 2010, miR-17-92 Cluster: Ups and Downs in Cancer and Aging, Biogerontology, 11, 501, 10.1007/s10522-010-9272-9 · DOI
  46. Hackl, 2010, miR-17, miR-19b, miR-20a, and miR-106a Are down-Regulated in Human Aging, Aging Cell, 9, 291, 10.1111/j.1474-9726.2010.00549.x · DOI
  47. Feng, 2023, Decreased miR-451a in Cerebrospinal Fluid, a Marker for Both Cognitive Impairment and Depressive Symptoms in Alzheimer’s Disease, Theranostics, 13, 3021, 10.7150/thno.81826 · DOI
  48. Xie, 2015, Serum miR-206 and miR-132 as Potential Circulating Biomarkers for Mild Cognitive Impairment, J. Alzheimers Dis., 45, 721, 10.3233/JAD-142847 · DOI · Paper page
  49. Liang, 2023, Exosomal miR-532-5p Induced by Long-Term Exercise Rescues Blood-Brain Barrier Function in 5XFAD Mice via Downregulation of EPHA4, Aging Cell, 22, e13748, 10.1111/acel.13748 · DOI
  50. Cao, 2021, MicroRNA-195 Rescues ApoE4-Induced Cognitive Deficits and Lysosomal Defects in Alzheimer’s Disease Pathogenesis, Mol. Psychiatry, 26, 4687, 10.1038/s41380-020-0824-3 · DOI
  51. Wei, 2018, Serum Exosomal miR-223 Serves as a Potential Diagnostic and Prognostic Biomarker for Dementia, Neuroscience, 379, 167, 10.1016/j.neuroscience.2018.03.016 · DOI · Paper page
  52. Song, 2019, A Novel Mechanism of Synaptic and Cognitive Impairments Mediated via microRNA-30b in Alzheimer’s Disease, EBioMedicine, 39, 409, 10.1016/j.ebiom.2018.11.059 · DOI
  53. Kim, 2020, miR-16-5p Is Upregulated by Amyloid β Deposition in Alzheimer’s Disease Models and Induces Neuronal Cell Apoptosis through Direct Targeting and Suppression of BCL-2, Exp. Gerontol., 136, 110954, 10.1016/j.exger.2020.110954 · DOI
  54. Zhang, 2015, MiR-16 Regulates Cell Death in Alzheimer’s Disease by Targeting Amyloid Precursor Protein, Eur. Rev. Med. Pharmacol. Sci., 19, 4020
  55. Baudry, 2010, miR-16 Targets the Serotonin Transporter: A New Facet for Adaptive Responses to Antidepressants, Science, 329, 1537, 10.1126/science.1193692 · DOI · Paper page
  56. Zhu, 2012, MicroRNA-195 Downregulates Alzheimer’s Disease Amyloid-β Production by Targeting BACE1, Brain Res. Bull., 88, 596, 10.1016/j.brainresbull.2012.05.018 · DOI · Paper page
  57. Kanduri, 2011, Kcnq1ot1: A Chromatin Regulatory RNA, Semin. Cell Dev. Biol., 22, 343, 10.1016/j.semcdb.2011.02.020 · DOI
  58. Zhao, 2017, SZRD1 Is a Novel Protein That Functions as a Potential Tumor Suppressor in Cervical Cancer, J. Cancer, 8, 2132, 10.7150/jca.18806 · DOI
  59. Laugesen, 2016, Role of the Polycomb Repressive Complex 2 (PRC2) in Transcriptional Regulation and Cancer, Cold Spring Harb. Perspect. Med., 6, a026575, 10.1101/cshperspect.a026575 · DOI
  60. Blackledge, 2015, Targeting Polycomb Systems to Regulate Gene Expression: Modifications to a Complex Story, Nat. Rev. Mol. Cell Biol., 16, 643, 10.1038/nrm4067 · DOI
  61. Kouznetsova, 2019, Polycomb Repressive 2 Complex—Molecular Mechanisms of Function, Protein Sci., 28, 1387, 10.1002/pro.3647 · DOI
  62. Pollard, 1995, Ion Channel Hypothesis for Alzheimer Amyloid Peptide Neurotoxicity, Cell. Mol. Neurobiol., 15, 513, 10.1007/BF02071314 · DOI
  63. Koh, 2008, Amyloid-Beta-Induced Neurotoxicity Is Reduced by Inhibition of Glycogen Synthase Kinase-3, Brain Res., 1188, 254, 10.1016/j.brainres.2007.10.064 · DOI
  64. Lee, N., Youn, K., Yoon, J.-H., Lee, B., Kim, D.H., and Jun, M. (2023). The Role of Fucoxanthin as a Potent Nrf2 Activator via Akt/GSK-3β/Fyn Axis against Amyloid-β Peptide-Induced Oxidative Damage. Antioxidants, 12. · DOI
  65. Arcaro, M., Fenoglio, C., Serpente, M., Arighi, A., Fumagalli, G.G., Sacchi, L., Floro, S., D’Anca, M., Sorrentino, F., and Visconte, C. (2022). A Novel Automated Chemiluminescence Method for Detecting Cerebrospinal Fluid Amyloid-Beta 1-42 and 1-40, Total Tau and Phosphorylated-Tau: Implications for Improving Diagnostic Performance in Alzheimer’s Disease. Biomedicines, 10. · DOI
  66. Dubois, 2014, Advancing Research Diagnostic Criteria for Alzheimer’s Disease: The IWG-2 Criteria, Lancet Neurol., 13, 614, 10.1016/S1474-4422(14)70090-0 · DOI · Paper page

Cited by

Sorted by citations

See all 57

Metadata

Authors
Visconte, C., Fenoglio, C., Serpente, M., Muti, P., Sacconi, A., Rigoni, M., Arighi, A., Borracci, V., Arcaro, M., Arosio, B., Ferri, E., Golia, M. T., Scarpini, E., & Galimberti, D.
Affiliations
  • Department of Biomedical, Surgical and Dental Sciences, University of Milan, 20122 Milan, Italy
  • Neurodegenerative Diseases Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
  • UOSD Clinical Trial Center, Biostatistics and Bioinformatics, Regina Elena National Cancer Institute—IRCCS, 00144 Rome, Italy
  • Department of Clinical Sciences and Community Health, University of Milan, 20122 Milan, Italy
  • Geriatric Unit, Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico, 20122 Milan, Italy
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2023-09-29
Volume
24
Issue
19
Pages
14749
ISSN
1422-0067
Cited by
57
References
5
Language
en
Funders
  • Italian Ministry of Health (RF-2018-12365333, GR-2019-12369100, 825664)
  • Dino Ferrari Center (RF-2018-12365333, GR-2019-12369100, 825664)
  • Fondazione Gigi (RF-2018-12365333, GR-2019-12369100, 825664)
  • Pupa Ferrari Onlus (RF-2018-12365333, GR-2019-12369100, 825664)
  • Italian Ministry of University and Research (RF-2018-12365333, GR-2019-12369100, 825664)

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