This article investigates the quality of the Romanian population data for the years 1966–2018. The following age heaping measures are being used, each with its recommended population age span: Whipple, Myers, Bachi, UNASA, Kannisto, Coale and Li. In addition, the Kannisto measure of old age overstatement is used. Our analysis corroborates the results of our measures with the demographic characteristic of the Romanian population. We aim to establish whether; (a) the data spanning the time interval 1966–1989 is more accurate than the data collected during the 1990–2018 interval, (b) the Romanian data is at least of comparable quality with the tier 2 HMD data and/or (c) the Romanian data is not grossly inaccurate and therefore suitable for research. We find support for (a) and, cautioning against the live data for the time interval 2002–2018, for (b) and (c). Limitations aside (no through overstatement or cohort analysis), this is the first study to attempt such evaluation and hopefully not the last.
Citation: Iulia Toropoc. Population data quality checks: Romanian adult deaths and lives, an evaluation[J]. National Accounting Review, 2023, 5(3): 282-297. doi: 10.3934/NAR.2023017
| [1] | Sisi Qi, Youyu Sheng, Ruiming Hu, Feng Xu, Ying Miao, Jun Zhao, Qinping Yang . Genome-wide expression profiling of long non-coding RNAs and competing endogenous RNA networks in alopecia areata. Mathematical Biosciences and Engineering, 2021, 18(1): 696-711. doi: 10.3934/mbe.2021037 |
| [2] | Xudan Ma, Qijun Zhang, Haihong Zhu, Kefeng Huang, Weina Pang, Qin Zhang . Establishment and analysis of the lncRNA-miRNA-mRNA network based on competitive endogenous RNA identifies functional genes in heart failure. Mathematical Biosciences and Engineering, 2021, 18(4): 4011-4026. doi: 10.3934/mbe.2021201 |
| [3] | Ziyu Wu, Sugui Wang, Qiang Li, Qingsong Zhao, Mingming Shao . Identification of 10 differently expressed lncRNAs as prognostic biomarkers for prostate adenocarcinoma. Mathematical Biosciences and Engineering, 2020, 17(3): 2037-2047. doi: 10.3934/mbe.2020108 |
| [4] | Yong Ding, Jian-Hong Liu . The signature lncRNAs associated with the lung adenocarcinoma patients prognosis. Mathematical Biosciences and Engineering, 2020, 17(2): 1593-1603. doi: 10.3934/mbe.2020083 |
| [5] | Lishui Shen, Xiaofeng Hu, Ting Chen, Guilin Shen, Dong Cheng . Integrated network analysis to explore the key mRNAs and lncRNAs in acute myocardial infarction. Mathematical Biosciences and Engineering, 2019, 16(6): 6426-6437. doi: 10.3934/mbe.2019321 |
| [6] | Jie Chen, Jinggui Chen, Bo Sun, Jianghong Wu, Chunyan Du . Integrative analysis of immune microenvironment-related CeRNA regulatory axis in gastric cancer. Mathematical Biosciences and Engineering, 2020, 17(4): 3953-3971. doi: 10.3934/mbe.2020219 |
| [7] | Shuai Miao, Lijun Wang, Siyu Guan, Tianshu Gu, Hualing Wang, Wenfeng Shangguan, Weiding Wang, Yu Liu, Xue Liang . Integrated whole transcriptome analysis for the crucial regulators and functional pathways related to cardiac fibrosis in rats. Mathematical Biosciences and Engineering, 2023, 20(3): 5413-5429. doi: 10.3934/mbe.2023250 |
| [8] | Yun-xiang Li, Shi-ming Wang, Chen-quan Li . Four-lncRNA immune prognostic signature for triple-negative breast cancer. Mathematical Biosciences and Engineering, 2021, 18(4): 3939-3956. doi: 10.3934/mbe.2021197 |
| [9] | Xiaoliang Jiang, Junjian Xiao, Qile Zhang, Lihui Wang, Jinyun Jiang, Kun Lan . Improved U-Net based on cross-layer connection for pituitary adenoma MRI image segmentation. Mathematical Biosciences and Engineering, 2023, 20(1): 34-51. doi: 10.3934/mbe.2023003 |
| [10] | Xiaodong Xia, Manman Wang, Jiao Li, Qiang Chen, Heng Jin, Xue Liang, Lijun Wang . Identification of potential genes associated with immune cell infiltration in atherosclerosis. Mathematical Biosciences and Engineering, 2021, 18(3): 2230-2242. doi: 10.3934/mbe.2021112 |
This article investigates the quality of the Romanian population data for the years 1966–2018. The following age heaping measures are being used, each with its recommended population age span: Whipple, Myers, Bachi, UNASA, Kannisto, Coale and Li. In addition, the Kannisto measure of old age overstatement is used. Our analysis corroborates the results of our measures with the demographic characteristic of the Romanian population. We aim to establish whether; (a) the data spanning the time interval 1966–1989 is more accurate than the data collected during the 1990–2018 interval, (b) the Romanian data is at least of comparable quality with the tier 2 HMD data and/or (c) the Romanian data is not grossly inaccurate and therefore suitable for research. We find support for (a) and, cautioning against the live data for the time interval 2002–2018, for (b) and (c). Limitations aside (no through overstatement or cohort analysis), this is the first study to attempt such evaluation and hopefully not the last.
Long noncoding RNAs (lncRNAs) were a type of ncRNAs longer than 200 bps, which could not be translated to proteins [1,2]. LncRNAs comprised antisense RNA, intronic transcripts, promoter-related lncRNA, long intergenic ncRNAs (lincRNA), and UTR associated lncRNAs [3]. Previous studies indicated lncRNAs played crucial roles in cancer progression and were involved in regulating cancer cell proliferation, apoptosis, autophagy and metastasis [4,5]. LncRNAs could regulate targets transcription via recruiting epigenetic modification regulators [6] and transcription factors [7]. LncRNAs could also regulate targets post-transcriptional expression via sponging miRNAs [8]. For instance, lncRNA PTENP1 could increase PTEN levels via sponging PTEN-targeting miRNAs [9,10]. PCGEM1 and PRNCR1 interacted with androgen receptor to promote transcriptional activity [11].
Pituitary adenomas (PA) is one of the most frequent types of intracranial neoplasms [12]. Surgery remains the first line of treatment for the majority of patients, but it cannot effectively control invasive pituitary adenomas [13]. In the past decades, emerging efforts were paid to understand the mechanisms underlying PA. Fukuoka et al. identifed EGFR as a therapeutic target for ACTH-secreting PA [14]. ADAM12 and MMP-14 were reported to regulate PA cavernous sinus invasion [15]. Meanwhile, non-coding RNAs (ncRNAs) were also found to play crucial roles in PA progression. For instance, miR-23b and miR-130b was reduced in PA [16]. miR-26a regulated cell cycle in ACTH-secreting PA by suppressing PRKCD [17]. Very interestingly, recent studies showed lncRNAs were dysregulated and played either oncogenetic or tumor suppressive roles in pituitary adenomas. For example, IFNG-AS1 interacted with ESRP2 to promote PA progression [18]. SNHG1 could sponge miR-302/372/373/520 cluster to active Wnt signaling in PA [19]. LncRNA MEG3 served as a tumor suppressor in PA [20]. Exploring the roles of lncRNAs in PA might shed light on the biology behind this disease and could prove novel potential biomarkers for PA prognosis.
In this study, we performed analysis of GSE26966 dataset to identify differently expressed lncRNAs in PA. In order to reveal the mechanism and function roles of them, we constructed lncRNA co-expression, lncRNA-protein interaction, and ceRNA networks in PA. We thought this study could provide novel clues to verify lncRNAs could be potential targets in PA diagnosis and therapy.
GSE26966 [21] dataset was used to identify dysregulated lncRNAs in PA, which was downloaded from GEO database (http://www.ncbi.nlm.nih.gov/geo/). GSE26966 included nine normal samples and 14 PA tissues. This dataset was normalized using robust multi-array average (RMA) method under R 2.6.2 statistical software with affy package from BioConductor. LncRNAs and mRNAs with fold changes (FC) ≥ 2 and P values less than 0.05 were considered as differentially expressed genes (DEGs).
Protein-protein interaction network (PPI) was constructed by STRING online software (https://string-db.org/). Combined score > 0.4 was considered as reliable. The PPI network was visualized using software Cytoscape software (http://www.cytoscape.org).
Functional and pathway enrichment analyses were conducted using the DAVID software (https://david.ncifcrf.gov/tools.jsp). The p value < 0.05 was set as the threshold.
Human pituitary tumour tissue samples were obtained from patients who underwent surgery at Department of Neurosurgery of Shanghai University of Traditional Chinese Medicine (Shanghai, China) between 2015 and 2017. Non-neoplastic pituitary gland specimens were obtained from autopsy. Tissues were frozen and stored in liquid nitrogen. All cases were pathologically diagnosed. All of the patients were provided written informed consent. The use of human specimens in this study was permitted by the local ethics committee at Shanghai University of Traditional Chinese Medicine.
Total RNA was extracted from the cells using TRIzol reagent (Invitrogen). cDNA was synthesized using the Takara PrimeScriptTM RT reagent kit. QPCR analysis using SYBR Green Supermix reagent (Invitrogen). The PCR primers used in this study included, ACTB-F 5'-CCTCTCCCAAGTCCACACAG-3' and -R 5'-GGGCACGAAGGCTCATCATT-3'; SNHG5-F 5'-CGCTTGGTTAAAACCTGACACT-3' and -R 5'-CCAAGACAATCTGGCCTCTATC-3'; SNHG7-F 5'-GTTGGGGTGTTGGCATTCTTGTT-3' and -R 5'-TGGTCAGCCTGGTCACTCTGG-3'; KCNQ1OT1-F 5'-AGGGTGACAGTGTTTCATAGGCT-3' and -R 5'-GAGGCACATTCATTCGTTGGT-3'; MEG3-F 5'-CTGCCCATCTACACCTCACG-3' and -R 5'-CTCTCCGCCGTCTGCGCTAGGGGCT-3'; Relative gene expression was analyzed using the 2-ΔΔCt method.
Statistical comparisons between groups were performed using T-test or Mann-Whitney U-test according to the test condition. A p < 0.05 was considered statistical significance with a 95% confidence level.
In this study, we identify differentially expressed mRNAs and lncRNAs in PA using GSE26966 dataset, which included 9 normal and 14 PA samples. We found 115 lncRNAs were overexpressed and 98 lncRNAs expression were suppressed in PA compared to normal samples (Figure 1A). Meanwhile, a total of 1258 mRNAs were overexpressed and 2584 mRNAs expression were reduced in PA (Figure 1B). Hierarchical clustering analysis was performed to show differentially expressed genes in PA.
Meanwhile, we constructed lncRNA co-expression networks to reveal the potential functions and mechanisms of differently expressed lncRNAs in PA. We next calculated Spearman Rank Correlation of lncRNA-mRNA pairs using online Co-LncRNA system (http://bio-bigdata.hrbmu.edu.cn/Co-LncRNA/) and selected lncRNA-mRNA pairs with absolute value ≥ 0.9 for co-expression network construction. This network included 118 lncRNAs and 542 mRNAs (Figure 2).
A few lncRNAs were observed to play more important roles in PA tumorigenesis, such as MEG3, SNHG7, MIR612, PWAR6, RP11-119F7.5, KCNQ1OT1, CELP, LINC00511, and LOC100129175. These lncRNAs co-expressed with more than 20 mRNAs. Among them, the roles of MEG3 had been widely reported, which played as a tumor suppressor in PA.
The mechanisms underlying PA remained largely unclear. In the present study, we considered co-expressing mRNAs with absolute value ≥ 0.9 were the potential targets of differentially expressed lncRNAs in PA. Then, we performed bioinformatics analysis for dysregulated lncRNAs using their co-expressing mRNAs. Our analysis showed these lncRNAs were significantly related to regulating mechanical stimulus, gene expression, in utero embryonic development, transcription, long-term synaptic potentiation, JAK-STAT cascade, cell cycle arrest, negative regulation of transcription, aging, and response to X-ray (Figure 3A). KEGG pathway analysis showed lncRNAs were involved in FoxO pathway, HIF-1 pathway, Insulin pathway, Oxytocin pathway, Adipocytokine pathway, Rap1 pathway, TGF-beta pathway, Hippo pathway, Prolactin pathway, and MAPK pathway (Figure 3B).
In order to reveal the mechanisms of lncRNAs underlying PA tumorigenesis, we further constructed lncRNAs-RBPs interaction network and ceRNA network in PA. Starbase dataset was used to identify lncRNAs interacting proteins, which based on Clip-sequence analysis. As present in Figure 4, the lncRNAs-RBPs interaction network included 33 RBPs and 53 lncRNAs. Three lncRNAs (SNHG5, RP6-24A23.7 and SNHG7) interacted with more than 20 RBPs and three RBPs (FUS, eIF4AIII, and UPF) interacted with more than 20 lncRNA, which were regarded as key regulators in this network.
LncRNAs mediated Competing endogenous RNA (ceRNAs) networks were constructed based on miRNA-targets prediction. The positively co-expressing lncRNA-mRNA pairs were considered for ceRNA construction. Next, the miRNAs targeted by both lncRNA and mRNA were identified as the bridge mediated the regulation between lncRNA and mRNA using TargetScan and Starbase dataset. A total of 25 lncRNAs, 58 miRNAs and 67 mRNAs were included in this ceRNA. KCNQ1OT1, SNHG7, LINC00472, and C9orf72 sponges more than 20 miRNAs and played crucial roles in PA (Figure 5).
We further validate our findings by detecting the expression levels of several key lncRNAs of regulatory networks in the PA samples using RT-PCR, including KCNQ1OT1, SNHG7, SNHG5, and MEG3 expression in PA. Microarray analysis showed these lncRNAs were remarkably down-regulated in PA. RT-PCR validation showed that the expressions of KCNQ1OT1, SNHG7, SNHG5, and MEG3 were down-regulated in PA samples (Figure 6A–E). Our results were consistent with the analysis of microarray.
Pituitary adenomas (PA) is one of the most frequent types of intracranial neoplasms [12]. However, the mechanisms underlying PA progression and novel biomarkers for the prognosis of PA remained largely unclear. Recently, lncRNAs were reported to play crucial roles in the tumorigenesis, cancer proliferation, and metastasis. In PA, a few lncRNAs were revealed to be involved in the cancer progression. For instance, H19 inhibits PA cell proliferation by disrupting 4E-BP1/Raptor interaction [22], IFNG-AS1 played oncogenic roles in PA by binding to ESRP2 [18], and E2F1 regulating CCAT2 was reported as an oncogene in PA via interacting with PTTG1 [23]. Of note, a recent study performed comprehensive analysis for dysregulated lncRNAs and mRNAs in PA using RNA-sequence [24]. However, the expression pattern and potential functions of lncRNAs in PA remained to be further investigated.
This study identified differentially expressed mRNAs and lncRNAs in PA by reannotating and analyzing GSE26966 dataset. A total of 1258 overexpressed and 2584 down-regulated mRNAs, 115 overexpressed and 98 down-regulated lncRNAs were identified in PA. We observed a few lncRNAs served as key lncRNAs in PA, including such as MEG3, SNHG7, MIR612, PWAR6, RP11-119F7.5, KCNQ1OT1, CELP, LINC00511, and LOC100129175. Among these lncRNAs, MEG3 was highly expressed in normal samples but decreased in cancer samples [25]. MEG3 played important roles in cancer initiation, growth, chemo-resistance, and metastasis. In PA, MEG3 levels were decreased in tumor tissues compared to normal samples [26]. In vivo and in vitro experiments showed MEG3 remarkably inhibited tumor growth by inducing G1 phase arrest [20]. SNHG7 was observed to be overexpressed and served as an oncogene in multiple cancers, including lung cancer [27], gastric cancer [28], and bladder cancer [29]. GO and KEGG analysis showed these lncRNAs were involved in regulating mechanical stimulus, gene expression, JAK-STAT cascade, cell cycle arrest, FoxO pathway, HIF-1 pathway, Insulin pathway, Oxytocin pathway, and MAPK pathway.
LncRNAs were involved in the regulation of chromatin interactions, gene transcription, mRNA stability and translation by interacting with other types of RNA, proteins and DNAs [30]. For example, XIST regulated X chromosome inactivation by localizing to the X chromosome to recruit multiple proteins, including HDAC3 [31]. LncRNA p21 was induced by p53 and regulate gene expression by binding to hnRNP-K under DNA damage. Competing endogenous RNA (ceRNAs) played a crucial role in the regulation of tumor-related pathways. LncRNA GAS5 regulate PTEN expression by sponging miR-222-3p in thyroid cancer [32]. SNHG16 induced esophagus cancer growth and metastasis by acting as a ceRNA to sponge miR-140-5p [33]. LncRNA ZFAS1 promoted osteosarcoma progression and metastasis by sponging miR-486. In present study, we constructed lncRNAs-RBPs interaction network and ceRNA network in PA. The lncRNAs-RBPs interaction network included 33 RBPs and 53 lncRNAs. From which, we identified SNHG5, RP6-24A23.7 and SNHG7 as core lncRNAs by potentially interacting with more than 20 RBPs. Meanwhile. We found FUS, eIF4AIII, and UPF may play key roles in mediating the effects of dysregulated lncRNAs on PA progression. Meanwhile, we found KCNQ1OT1, SNHG7, LINC00472, and C9orf72 played key roles in PA which sponged about 20 miRNAs. Recent studies showed KCNQ1OT1 was dysregulated in multiple cancers. In lung cancer, KCNQ1OT1 modulated miRNA-27b-3p/HSP90AA1 axis to promote tumor progression [34]. In breast cancer, KCNQ1OT1 was overexpressed in tumor samples and induced cancer proliferation by regulating miR-145/CCNE2 axis [35]. However, the roles of KCNQ1OT1 in PA remained unclear. Our study for the first time revealed KCNQ1OT1 may play key roles in PA. Finally, we validate KCNQ1OT1, SNHG7, MEG3, and SNHG5expression in PA.
In conclusion, this study identified differently expressed mRNAs and lncRNAs by using GSE26966 database. Furthermore, we constructed lncRNA-mRNA co-expression, lncRNA-RBP interaction and ceRNA networks. Bioinformatics analysis showed these lncRNAs were involved in regulating mechanical stimulus, gene expression, JAK-STAT cascade, cell cycle arrest, FoxO signaling, HIF-1 signaling, Insulin signaling, Oxytocin signaling, and MAPK signaling. We also showed KCNQ1OT1, SNHG7, MEG3, and SNHG5 were down-regulated in PA. Our findings could provide a novel insight to understand the mechanisms of lncRNAs underlying PA pathogenesis and identify new biomarkers for PA.
The authors declare that they have no conflict of interest.
| [1] |
Abbott A (2006) Reconceptualizing knowledge accumulation in sociology. Am Soc 37: 57–66. https://doi.org/10.1007/s12108-006-1005-9 doi: 10.1007/s12108-006-1005-9
|
| [2] |
Abbott A (2018) Varieties of normative inquiry: Moral alternatives to politicization in sociology. Am Soc 49: 158–180. https://doi.org/10.1007/s12108-017-9367-8 doi: 10.1007/s12108-017-9367-8
|
| [3] |
A'Hearn B, Baten J, Crayen D (2009) Quantifying quantitative literacy: Age heaping and the history of human capital. J Eco Hist 69: 783–808. https://doi.org/10.1017/S0022050709001120 doi: 10.1017/S0022050709001120
|
| [4] |
A'Hearn, B, Delfino A, Nuvolari A (2021) Rethinking age heaping: a cautionary tale from nineteenth-century Italy. Eco Hist Rev 75: 111–137. https://doi.org/10.1111/ehr.13087 doi: 10.1111/ehr.13087
|
| [5] | Bainame K, Letamo G (2015) Evaluation of Data Quality of the Botswana 2011 Population and Housing Census. Botswana Notes and Records 46. |
| [6] |
Bairagi R, Aziz KMA, Chowdhury MK, et al. (1982) Age misstatement for young children in rural Bangladesh. Demography 19: 447–458. https://doi.org/10.2307/2061012 doi: 10.2307/2061012
|
| [7] | Bucur B (2016) Population Health in Interwar Romania Reflected in the Sociological School of Bucharest's Research and Publications, In: Marinescu, V., Mitu, B., Valentina Marinescu and Bianca Mitu Health and the Media: Essays on the Effects of Mass Communication, Jefferson [NC, USA]: McFarland & Co, 215–240. |
| [8] |
Byerle D, Terera G (1981) Factors affecting reliability in age estimation in rural West Africa: A statistical analysis. Popul Stud 35: 455–465. https://doi.org/10.1080/00324728.1981.11878517 doi: 10.1080/00324728.1981.11878517
|
| [9] |
Caldwell JC (1966) Study of age misstatement among young children in Ghana. Demography 3: 477–490. https://doi.org/10.2307/2060173 doi: 10.2307/2060173
|
| [10] |
Caldwell JC, Igun AA (1971) An experiment with census-type age enumeration in Nigeria. Popul Stud 25: 287–302. https://doi.org/10.1080/00324728.1971.10405804 doi: 10.1080/00324728.1971.10405804
|
| [11] |
Coale AJ, Kisker EE (1986) Mortality crossovers: Reality or bad data? Popu stud 40: 389–401. https://doi.org/10.1080/0032472031000142316 doi: 10.1080/0032472031000142316
|
| [12] |
Coale AJ, Li S (1991) The effect of age misreporting in China on the calculation of mortality rates at very high ages. Demography 28: 293–301. https://doi.org/10.2307/2061281 doi: 10.2307/2061281
|
| [13] |
Chemhaka GB, Odimegwu C, Zwane EN (2016) Is Swaziland census data suitable for fertility measurement? Genus 72. https://doi.org/10.1186/s41118-016-0010-2 doi: 10.1186/s41118-016-0010-2
|
| [14] | Dechter AR, Preston SH (1991) Age misreporting and its effects on adult mortality estimates in Latin America. Popul Bull UN, 1–16. |
| [15] | Fajardo-González J, Attanasio L, Trang Ha J (2014) An Assessment of the Age Reporting in the IPUMS-I Microdata. Available from: https://paa2014.populationassociation.org/papers/140099. |
| [16] | Golopentia A, Grigorescu DC (1948) Populatia Republicii Populare Romane la 25 Ianuarie 1948. Rezultatele provizorii ale recensamantului. Extras din Probleme economice, nr 2. Institutul National de Statistica. |
| [17] | Jdanov DA, Jasilionis D, Soroko EL, et al. (2008) Beyond the Kannisto-Thatcher database on old age mortality: An assessment of data quality at advanced ages. Available from: https://www.demogr.mpg.de/papers/working/wp-2008-013.pdf. |
| [18] | Kannisto V (1999) Assessing the Information on Age at Death of Old Persons in National Vital Statistics, In: Validation of Exceptional Longevity, Odense: Odense University Press, 6: 240–249. |
| [19] |
Lee MM, Zhang N (2017) Legibility and the informational foundations of state capacity. J Polit 79: 118–32. https://doi.org/10.1086/688053 doi: 10.1086/688053
|
| [20] | Mihaescu C, Dumitrescu I, Mirica A (2018) Romania: un secol de istorie. Date statistice. Andrei Tudorel (coord). Bucuresti: Editura Institutului National de Statistica, 2018. Populatia, 12–48. |
| [21] |
Mokyr J, Gráda CÓ (1982) Emigration and poverty in prefamine Ireland. Explor Econ Hist 19: 360–384. https://doi.org/10.1016/0014-4983(82)90008-0 doi: 10.1016/0014-4983(82)90008-0
|
| [22] | Penina O, Jdanov D, Grigoriev P (2015) Producing reliable mortality estimates in the context of distorted population statistics: the case of Moldova. Available from: https://www.demogr.mpg.de/papers/working/wp-2015-011.pdf. |
| [23] | Pisica S, Murgescu B, Sora FA, et al. (2018) Romania: un secol de istorie. Date statistice. Andrei Tudorel (coord). Bucuresti: Editura Institutului National de Statistica, 2018. Forta de munca, 49–65. |
| [24] |
Preston SH, Elo IT, PRESTON SH (1999) Effects of age misreporting on mortality estimates at older ages. Popul Stud 53: 165–177. https://doi.org/10.1080/00324720308075 doi: 10.1080/00324720308075
|
| [25] |
Pripoaie R, Cretu CM, Turtureanu AG, et al. (2022) A Statistical Analysis of the Migration Process: A Case Study—Romania. Sustainability 14. https://doi.org/10.3390/su14052784 doi: 10.3390/su14052784
|
| [26] | Sandu D (2018) Migratia temporara in strainatate (1990–2016), In: Ghețău, V., Demografia Romaniei, Editura Academiei Române, 245–278. |
| [27] | Shryock HS, Siegel JS (1975) The methods and materials of demography. |
| [28] |
Simandan D (2019) Revisiting positionality and the thesis of situated knowledge. Dialogues Hum Geogr 9: 129–149. https://doi.org/10.1177/20438206198500 doi: 10.1177/20438206198500
|
| [29] |
Simandan D (2010) Roads to perdition in the knowledge economy. Environ Plan 42: 1519–1520. https://doi.org/10.1068/a4324 doi: 10.1068/a4324
|
| [30] | Yi Z, Gu D (2008) Reliability of age reporting among the Chinese oldest-old in the CLHLS datasets. In: Healthy longevity in China, 61–78, Springer, Dordrecht. |
| [31] | United Nations (1955) Manual Ⅱ: Methods of appraisal of quality of basic data for population estimates (United Nations Publications, Sales No. 56.ⅩⅢ.2). Chapter 3: The Accuracy of Age and Sex Statistics. |
| [32] | United Nations, Department of Economic and Social Affairs Population Division. World Prospects 2022. Romania Demographic Profiles, Line Charts, 2022. Available from: https://population.un.org/wpp/Graphs/DemographicProfiles/Line/642. |
 NAR-05-03-017-s001.pdf |
 |
| 1. | Hengzhou Lin, Dahui Zuo, Jiabin He, Tao Ji, Jianzhong Wang, Taipeng Jiang, Long Noncoding RNA WEE2-AS1 Plays an Oncogenic Role in Glioblastoma by Functioning as a Molecular Sponge for MicroRNA-520f-3p, 2021, 28, 0965-0407, 591, 10.3727/096504020X15982623243955 | |
| 2. | Peng Zhao, Jianhua Cheng, Bin Li, Ding Nie, Hongyun Wang, Chuzhong Li, Songbai Gui, Yazhuo Zhang, LncRNA PCAT6 regulates the progression of pituitary adenomas by regulating the miR-139-3p/BRD4 axis, 2021, 21, 1475-2867, 10.1186/s12935-020-01698-7 | |
| 3. | Chao Peng, Shuaikai Wang, Jinxiu Yu, Xiaoyi Deng, Huiyu Ye, Zhishan Chen, Hongru Yao, Hanjia Cai, Yanli Li, Yong Yuan, Riccardo Alessandro, lncRNA–mRNA Expression Patterns in Invasive Pituitary Adenomas: A Microarray Analysis, 2022, 2022, 2314-6141, 1, 10.1155/2022/1380485 | |
| 4. | Sajad Najafi, Soudeh Ghafouri-Fard, Bashdar Mahmud Hussen, Hazha Hadayat Jamal, Mohammad Taheri, Mohammad Hallajnejad, Oncogenic Roles of Small Nucleolar RNA Host Gene 7 (SNHG7) Long Noncoding RNA in Human Cancers and Potentials, 2022, 9, 2296-634X, 10.3389/fcell.2021.809345 | |
| 5. | Haixia Wang, Hongbin Mou, Xiaolan Xu, Changhua Liu, Gang Zhou, Bo Gao, LncRNA KCNQ1OT1 (potassium voltage-gated channel subfamily Q member 1 opposite strand/antisense transcript 1) aggravates acute kidney injury by activating p38/NF-κB pathway via miR-212-3p/MAPK1 (mitogen-activated protein kinase 1) axis in sepsis, 2021, 12, 2165-5979, 11353, 10.1080/21655979.2021.2005987 | |
| 6. | Parisa Esmaeili Motlagh, Soudeh Ghafouri-Fard, Solat Eslami, Guive Sharifi, Mohammad Taheri, Expression assays of selected lncRNAs in non-functioning pituitary adenomas, 2024, 15, 2730-6011, 10.1007/s12672-024-01338-w | |
| 7. | Neda Rahimian, Amirhossein Sheida, Mohammadreza Rajabi, Mohammad Mahdi Heidari, Mohammad Tobeiha, Pegah Veradi Esfahani, Sahar Ahmadi Asouri, Michael R. Hamblin, Omid Mohamadzadeh, Alireza Motamedzadeh, Mahmood Khaksary Mahabady, Non-coding RNAs and exosomal non-coding RNAs in pituitary adenoma, 2023, 248, 03440338, 154649, 10.1016/j.prp.2023.154649 | |
| 8. | Farbod Esfandi, Seyed Amirmohsen Mirjafari, Negar Rezazadeh, Vidasadat Seyyedesfahani, Amir Nicknam, Solat Eslami, Guive Sharifi, Nader Akbari Dilmaghani, Arezou Sayad, Soudeh Ghafouri-Fard, Differential expression of cancer-related lncRNAs in different subtypes of pituitary adenomas, 2024, 260, 03440338, 155421, 10.1016/j.prp.2024.155421 | |
| 9. | Ling Wang, Dingkai Xu, Regulation of long noncoding RNAs in the pathogenesis and clinical implications of pituitary adenomas, 2023, 11, 2050-4527, 10.1002/iid3.1047 | |
| 10. | Xiaohui Xing, Peng Xu, Xiaoyang Xing, Zhentao Xu, Zhen Huang, Zhongchen Li, Xueyuan Li, Yilei Xiao, Effects of ADSC-Derived Exosome LRRC75A-AS1 on Anti-inflammatory Function After SCI, 2024, 0273-2289, 10.1007/s12010-023-04836-9 | |
| 11. | Soudeh Ghafouri-Fard, Farzaneh Abbasi, Amir Nicknam, Bashdar Mahmud Hussen, Solat Eslami, Nader Akbari Dilmaghani, Mohammad Taheri, Guive Sharifi, Dysregulation of PVT1 and NEAT1 lncRNAs in pituitary adenomas, 2023, 248, 03440338, 154573, 10.1016/j.prp.2023.154573 | |
| 12. | Michelle Lynn Brinkmeier, Akima Semone George, Leonard Yan Ming Cheung, Ryan Edward Mills, Philippa Melamed, Sally Ann Camper, Long Noncoding RNAs Expressed in Mouse Pituitary Development and Mature Hormone-Producing Cells, 2024, 165, 1945-7170, 10.1210/endocr/bqae147 |
Iulia Toropoc. Population data quality checks: Romanian adult deaths and lives, an evaluation[J]. National Accounting Review, 2023, 5(3): 282-297. doi: 10.3934/NAR.2023017
DownLoad:
