{"dcterms:modified":"2022-01-05","dcterms:creator":"A Dataverse Instance","@type":"ore:ResourceMap","@id":"https://dataverse.unimi.it/api/datasets/export?exporter=OAI_ORE&persistentId=doi:10.13130/RD_UNIMI/OXXJ1N","ore:describes":{"biomedical:Design Type":"Technological Design","geospatial:Geographic Coverage":{"geographicCoverage:Country / Nation":"Italy","geographicCoverage:State / Province":"Milan"},"Title":"Investigation on ncRNA in senescence in senescence cellular models","biomedical:Factor Type":["Biomarkers","Cell Type/Cell Line","Passages","Time Point","Treatment Compound","Treatment Type"],"biomedical:Organism":"Homo sapiens","biomedical:Measurement Type":["cell counting","metabolite profiling","protein expression profiling","transcription profiling"],"biomedical:Technology Type":["culture based drug susceptibility testing, single concentration","gel electrophoresis","real time PCR"],"Author":{"author:Name":"Battaglia, Cristina","author:Affiliation":"University of Milan","Identifier Scheme":"ORCID","Identifier":"0000-0003-3025-9657"},"biomedical:Cell Type":["SHSY5Y","Normal Human Dermal Fibroblasts (NHDF)","HUVEC"],"citation:Contact":{"datasetContact:Name":"Battaglia, Cristina","datasetContact:Affiliation":"University of Milan"},"citation:Description":{"dsDescription:Text":"Cellular senescence is a hallmark of aging and is the result of a variety of stresses, such as telomere attrition, DNA damage and mitochondrial dysfunction. Moreover, senescent cells display a typical secretory phenotype, which involves the secretion of several inflammatory factors, as well as increased levels of beta-galactosidase activity in the lysosomes. Senescent cells accumulate during aging and have been implicated in promoting a variety of age-related diseases, including neurodegenerative diseases.\r\nAutophagy, the degradation system whereby the cells recycle dysfunctional proteins and damaged organelles, is one of the processes affected by senescence, but it is also able to induce senescence. Among the different mechanisms controlling the senescence status and the autophagic process, a key role of non-coding RNAs (ncRNAs), both miRNAs and long non-coding RNAs (lncRNAs), is now clearly emerging.\r\n\r\nAIM OF THE PROJECT\r\nWith the aim to explore a possible dysregulation of ncRNAs in senescence, we will measured by qPCR the levels of a set of miRNAs and lncRNAs associated to autophagy, senescence and aging in different cellular models of replicative and chemical induced senescence.\r\nSUPPORTED BY\r\nThe investigation was supported by PSD2020, University of Milan to Cristina Battaglia and Marco Venturin."},"Subject":"Medicine, Health and Life Sciences","citation:Keyword":{"keyword:Term":"cellular senescence, Noncoding RNA, Long, lncRNA, Untranslated RNA, Real-Time PCR","keyword:Vocabulary":"MeSH","keyword:Vocabulary URL":"https://www.ncbi.nlm.nih.gov/mesh"},"citation:Production Date":"2021-09-12","citation:Production Place":"Milan, Italy","Contributor":[{"contributor:Type":"Data Collector","contributor:Name":"Marco Venturin"},{"contributor:Type":"Data Curator","contributor:Name":"Cristina Battaglia"}],"Grant Information":{"grantNumber:Grant Agency":"University of Milan","grantNumber:Grant Number":"PSD-2020"},"citation:Depositor":"Battaglia, Cristina","Deposit Date":"2021-12-29","Time Period Covered":{"timePeriodCovered:Start":"2020-11-01","timePeriodCovered:End":"2021-09-28"},"citation:Date of Collection":{"dateOfCollection:Start":"2020-11-01"},"Kind of Data":["aggregate data","exprimental data","textual data"],"@id":"doi:10.13130/RD_UNIMI/OXXJ1N","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"1.0","schema:datePublished":"2022-01-05","schema:name":"Investigation on ncRNA in senescence in senescence cellular models","schema:dateModified":"2022-01-05 15:33:54.592","schema:license":"https://creativecommons.org/publicdomain/zero/1.0/","dvcore:fileTermsOfAccess":{"dvcore:fileRequestAccess":false},"schema:includedInDataCatalog":"ncRNAging","ore:aggregates":[{"schema:description":"POSTER was presented on  2021-09-27 during  BIOMETRA Workshop2021(Mlilan). The poster described the results obtained in  a model of replicative senescence of Normal Human Dermal Fibroblasts (NHDFs) in replicative senescence, following prolonged culturing: young (passage\r\n14,n=3) vs. old (passage 32-33, n=3). Cells characterization by multi-biomarker analyses; QPCR evaluation of ncRNAs expression of 15 lncRNAs and 9 \r\nmiRNAs previuosly selected by literature mining.","schema:name":"BIOMETRA_poster_2021_final.pdf","dvcore:restricted":false,"schema:version":2,"dvcore:datasetVersionId":459,"dvcore:categories":["Documentation"],"@id":"doi:10.13130/RD_UNIMI/OXXJ1N/GRMIFL","schema:sameAs":"https://dataverse.unimi.it/api/access/datafile/:persistentId?persistentId=doi:10.13130/RD_UNIMI/OXXJ1N/GRMIFL","@type":"ore:AggregatedResource","schema:fileFormat":"application/pdf","dvcore:filesize":1989331,"dvcore:storageIdentifier":"s3://4s-dataverse-unimi:17e0732e397-7ae1c866c828","dvcore:rootDataFileId":-1,"dvcore:checksum":{"@type":"MD5","@value":"46589242dc712ac816cf50b4fa89e665"}},{"schema:description":"POSTER presented by Sabrina Briguglio  titled \"Investigation of non-coding-RNAs targeting senescence and autophagy in “zombie” cells\" on 2021-23-09. The poster described the results obtained in two models of senescence. Normal Human Dermal Fibroblasts (NHDF) in replicative senescence, following prolonged culturing and\r\nneuroblastoma cell line (SH-SY5Y)  in oxidative stress-induced senescence, following treatment with H2O2. Cells characterization was performed by biochemical and molecular methods; the expression level of selected protein-coding RNAs  and non-coding RNAs  was evaluated by QPCR.","schema:name":"Briguglio_MasterThesis_Poster_2021.pdf","dvcore:restricted":false,"schema:version":3,"dvcore:datasetVersionId":459,"dvcore:categories":["Documentation"],"@id":"doi:10.13130/RD_UNIMI/OXXJ1N/9PPW1M","schema:sameAs":"https://dataverse.unimi.it/api/access/datafile/:persistentId?persistentId=doi:10.13130/RD_UNIMI/OXXJ1N/9PPW1M","@type":"ore:AggregatedResource","schema:fileFormat":"application/pdf","dvcore:filesize":1626792,"dvcore:storageIdentifier":"s3://4s-dataverse-unimi:17e0736b45a-8864d9d922be","dvcore:rootDataFileId":-1,"dvcore:checksum":{"@type":"MD5","@value":"63631f05ab57615338d87816999bb658"}}],"schema:hasPart":["doi:10.13130/RD_UNIMI/OXXJ1N/GRMIFL","doi:10.13130/RD_UNIMI/OXXJ1N/9PPW1M"]},"@context":{"Author":"http://purl.org/dc/terms/creator","Contributor":"http://purl.org/dc/terms/contributor","Deposit Date":"http://purl.org/dc/terms/dateSubmitted","Grant Information":"https://schema.org/sponsor","Identifier":"http://purl.org/spar/datacite/AgentIdentifier","Identifier Scheme":"http://purl.org/spar/datacite/AgentIdentifierScheme","Kind of Data":"http://rdf-vocabulary.ddialliance.org/discovery#kindOfData","Subject":"http://purl.org/dc/terms/subject","Time Period Covered":"https://schema.org/temporalCoverage","Title":"http://purl.org/dc/terms/title","author":"https://dataverse.org/schema/citation/author#","biomedical":"https://dataverse.unimi.it/schema/biomedical#","citation":"https://dataverse.org/schema/citation/","contributor":"https://dataverse.org/schema/citation/contributor#","datasetContact":"https://dataverse.org/schema/citation/datasetContact#","dateOfCollection":"https://dataverse.org/schema/citation/dateOfCollection#","dcterms":"http://purl.org/dc/terms/","dsDescription":"https://dataverse.org/schema/citation/dsDescription#","dvcore":"https://dataverse.org/schema/core#","geographicCoverage":"https://dataverse.unimi.it/schema/geospatial/geographicCoverage#","geospatial":"https://dataverse.unimi.it/schema/geospatial#","grantNumber":"https://dataverse.org/schema/citation/grantNumber#","keyword":"https://dataverse.org/schema/citation/keyword#","ore":"http://www.openarchives.org/ore/terms/","schema":"http://schema.org/","timePeriodCovered":"https://dataverse.org/schema/citation/timePeriodCovered#"}}