{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:38:53Z","timestamp":1777250333357,"version":"3.51.4"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Open Project Programme of the Key Base for Standardized Training for General Physicans, Zhongda Hospital, Southeast University","award":["ZDZYJD-QK-2022-5"],"award-info":[{"award-number":["ZDZYJD-QK-2022-5"]}]},{"name":"Key Research Base Foundation of Philosophy and Social Sciences of Jiangsu Education Department","award":["2021B05"],"award-info":[{"award-number":["2021B05"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mol Neurobiol"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s12035-026-05883-z","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:05:30Z","timestamp":1777248330000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Establishing a Diagnostic Model Based on Plasma CDR1as and Alpha-Synuclein for Mild Cognitive Impairment in Type 2 Diabetes"],"prefix":"10.1007","volume":"63","author":[{"given":"Wencan","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Canjun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanhu","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaohua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"5883_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2022.104642","volume":"137","author":"A Luo","year":"2022","unstructured":"Luo A, Xie Z, Wang Y, Wang X, Li S, Yan J, Zhan G, Zhou Z et al (2022) Type 2 diabetes mellitus-associated cognitive dysfunction: advances in potential mechanisms and therapies. Neurosci Biobehav Rev 137:104642. https:\/\/doi.org\/10.1016\/j.neubiorev.2022.104642","journal-title":"Neurosci Biobehav Rev"},{"issue":"3","key":"5883_CR2","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/S1474-4422(14)70249-2","volume":"14","author":"PS Koekkoek","year":"2015","unstructured":"Koekkoek PS, Kappelle LJ, van den Berg E, Rutten GE, Biessels GJ (2015) Cognitive function in patients with diabetes mellitus: guidance for daily care. Lancet Neurol 14(3):329\u2013340. https:\/\/doi.org\/10.1016\/S1474-4422(14)70249-2","journal-title":"Lancet Neurol"},{"key":"5883_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2019.100944","volume":"55","author":"M Xue","year":"2019","unstructured":"Xue M, Xu W, Ou YN, Cao XP, Tan MS, Tan L, Yu JT (2019) Diabetes mellitus and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 144 prospective studies. Ageing Res Rev 55:100944. https:\/\/doi.org\/10.1016\/j.arr.2019.100944","journal-title":"Ageing Res Rev"},{"issue":"1","key":"5883_CR4","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-019-0480-5","volume":"11","author":"H Shimada","year":"2019","unstructured":"Shimada H, Doi T, Lee S, Makizako H (2019) Reversible predictors of reversion from mild cognitive impairment to normal cognition: a 4-year longitudinal study. Alzheimers Res Ther 11(1):24. https:\/\/doi.org\/10.1186\/s13195-019-0480-5","journal-title":"Alzheimers Res Ther"},{"key":"5883_CR5","doi-asserted-by":"publisher","unstructured":"Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, Holtzman DM, Jagust W et al (2018) NIA-AA research framework: toward a biological definition of Alzheimer\u2019s disease. Alzheimers Dement 14(4):535\u2013562. https:\/\/doi.org\/10.1016\/j.jalz.2018.02.018","DOI":"10.1016\/j.jalz.2018.02.018"},{"issue":"5","key":"5883_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/ijms23052687","volume":"23","author":"M Michailidis","year":"2022","unstructured":"Michailidis M, Moraitou D, Tata DA, Kalinderi K, Papamitsou T, Papaliagkas V (2022) Alzheimer\u2019s disease as type 3 diabetes: common pathophysiological mechanisms between Alzheimer\u2019s disease and type 2 diabetes. Int J Mol Sci 23(5):2687. https:\/\/doi.org\/10.3390\/ijms23052687","journal-title":"Int J Mol Sci"},{"key":"5883_CR7","doi-asserted-by":"publisher","unstructured":"Martinez-Valbuena I, Valenti-Azcarate R, Amat-Villegas I, Riverol M, Marcilla I, de Andrea CE, S\u00e1nchez-Arias JA, Del Mar Carmona-Abellan M et al (2019) Amylin as a potential link between type 2 diabetes and Alzheimer disease. Ann Neurol 86(4):539\u2013551. https:\/\/doi.org\/10.1002\/ana.25570","DOI":"10.1002\/ana.25570"},{"issue":"8","key":"5883_CR8","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22083878","volume":"22","author":"I Piatkowska-Chmiel","year":"2021","unstructured":"Piatkowska-Chmiel I, Herbet M, Gawronska-Grzywacz M, Ostrowska-Lesko M, Dudka J (2021) The role of molecular and inflammatory indicators in the assessment of cognitive dysfunction in a mouse model of diabetes. Int J Mol Sci 22(8):3878. https:\/\/doi.org\/10.3390\/ijms22083878","journal-title":"Int J Mol Sci"},{"issue":"10","key":"5883_CR9","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1038\/nrneurol.2010.130","volume":"6","author":"C Sims-Robinson","year":"2010","unstructured":"Sims-Robinson C, Kim B, Rosko A, Feldman EL (2010) How does diabetes accelerate Alzheimer disease pathology. Nat Rev Neurol 6(10):551\u2013559. https:\/\/doi.org\/10.1038\/nrneurol.2010.130","journal-title":"Nat Rev Neurol"},{"key":"5883_CR10","doi-asserted-by":"publisher","unstructured":"Duran R, Pancur S, Bahadori\u00a0F (2022) The effect of type 2 diabetes mellitus on the development of alzheimer's disease and its molecular mechanism. Bezmialem Sci 10(1):114\u201322. https:\/\/doi.org\/10.14235\/bas.galenos.2020.4491","DOI":"10.14235\/bas.galenos.2020.4491"},{"issue":"10","key":"5883_CR11","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1038\/s41574-018-0048-7","volume":"14","author":"GJ Biessels","year":"2018","unstructured":"Biessels GJ, Despa F (2018) Cognitive decline and dementia in diabetes mellitus: mechanisms and clinical implications. Nat Rev Endocrinol 14(10):591\u2013604. https:\/\/doi.org\/10.1038\/s41574-018-0048-7","journal-title":"Nat Rev Endocrinol"},{"key":"5883_CR12","doi-asserted-by":"publisher","unstructured":"Dos Santos Matioli M, Suemoto CK, Rodriguez RD, Farias DS, da Silva MM, Leite R, Ferretti-Rebustini R, Farfel JM et al (2017) Diabetes is not associated with Alzheimer\u2019s disease neuropathology. J Alzheimers Dis 60(3):1035\u20131043. https:\/\/doi.org\/10.3233\/JAD-170179","DOI":"10.3233\/JAD-170179"},{"key":"5883_CR13","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.molmet.2018.01.010","volume":"9","author":"L Stoll","year":"2018","unstructured":"Stoll L, Sobel J, Rodriguez-Trejo A, Guay C, Lee K, Ven\u00f8 MT, Kjems J, Laybutt DR et al (2018) Circular RNAs as novel regulators of \u03b2-cell functions in normal and disease conditions. Mol Metab 9:69\u201383. https:\/\/doi.org\/10.1016\/j.molmet.2018.01.010","journal-title":"Mol Metab"},{"key":"5883_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/srep12453","volume":"5","author":"H Xu","year":"2015","unstructured":"Xu H, Guo S, Li W, Yu P (2015) The circular RNA Cdr1as, via miR-7 and its targets, regulates insulin transcription and secretion in islet cells. Sci Rep 5:12453. https:\/\/doi.org\/10.1038\/srep12453","journal-title":"Sci Rep"},{"issue":"12","key":"5883_CR15","doi-asserted-by":"publisher","DOI":"10.3390\/genes7120116","volume":"7","author":"Y Zhao","year":"2016","unstructured":"Zhao Y, Alexandrov PN, Jaber V, Lukiw WJ (2016) Deficiency in the ubiquitin conjugating enzyme UBE2A in Alzheimer\u2019s disease (AD) is linked to deficits in a natural circular miRNA-7 sponge (circRNA; ciRS-7). Genes 7(12):116. https:\/\/doi.org\/10.3390\/genes7120116","journal-title":"Genes"},{"issue":"7","key":"5883_CR16","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1111\/febs.14045","volume":"284","author":"Z Shi","year":"2017","unstructured":"Shi Z, Chen T, Yao Q, Zheng L, Zhang Z, Wang J, Hu Z, Cui H et al (2017) The circular RNA ciRS-7 promotes APP and BACE1 degradation in an NF-\u03baB-dependent manner. FEBS J 284(7):1096\u20131109. https:\/\/doi.org\/10.1111\/febs.14045","journal-title":"FEBS J"},{"key":"5883_CR17","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/978-981-13-1426-1_19","volume":"1087","author":"R Akhter","year":"2018","unstructured":"Akhter R (2018) Circular RNA and Alzheimer\u2019s disease. Adv Exp Med Biol 1087:239\u2013243. https:\/\/doi.org\/10.1007\/978-981-13-1426-1_19","journal-title":"Adv Exp Med Biol"},{"issue":"31","key":"5883_CR18","doi-asserted-by":"publisher","first-page":"13052","DOI":"10.1073\/pnas.0906277106","volume":"106","author":"E Junn","year":"2009","unstructured":"Junn E, Lee KW, Jeong BS, Chan TW, Im JY, Mouradian MM (2009) Repression of alpha-synuclein expression and toxicity by microRNA-7. Proc Natl Acad Sci U S A 106(31):13052\u201313057. https:\/\/doi.org\/10.1073\/pnas.0906277106","journal-title":"Proc Natl Acad Sci U S A"},{"key":"5883_CR19","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.parkreldis.2020.03.014","volume":"73","author":"E Emmanouilidou","year":"2020","unstructured":"Emmanouilidou E, Papagiannakis N, Kouloulia S, Galaziou A, Antonellou R, Papadimitriou D, Athanasiadou A, Bozi M et al (2020) Peripheral alpha-synuclein levels in patients with genetic and non-genetic forms of Parkinson\u2019s disease. Parkinsonism Relat Disord 73:35\u201340. https:\/\/doi.org\/10.1016\/j.parkreldis.2020.03.014","journal-title":"Parkinsonism Relat Disord"},{"issue":"3","key":"5883_CR20","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1016\/j.ajpath.2014.11.016","volume":"185","author":"ME Oskarsson","year":"2015","unstructured":"Oskarsson ME, Paulsson JF, Schultz SW, Ingelsson M, Westermark P, Westermark GT (2015) In vivo seeding and cross-seeding of localized amyloidosis: a molecular link between type 2 diabetes and Alzheimer disease. Am J Pathol 185(3):834\u2013846. https:\/\/doi.org\/10.1016\/j.ajpath.2014.11.016","journal-title":"Am J Pathol"},{"issue":"Pt 1","key":"5883_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijbiomac.2024.135137","volume":"279","author":"Z Song","year":"2024","unstructured":"Song Z, Tang H, Gatch A, Sun Y, Ding F (2024) Islet amyloid polypeptide fibril catalyzes amyloid-\u03b2 aggregation by promoting fibril nucleation rather than direct axial growth. Int J Biol Macromol 279(Pt 1):135137. https:\/\/doi.org\/10.1016\/j.ijbiomac.2024.135137","journal-title":"Int J Biol Macromol"},{"key":"5883_CR22","doi-asserted-by":"publisher","unstructured":"Meng L, Li Y, Liu C, Zhang G, Chen J, Xiong M, Pan L, Zhang X et al (2023) Islet amyloid polypeptide triggers \u03b1-synuclein pathology in Parkinson\u2019s disease. Prog Neurobiol 226:102462. https:\/\/doi.org\/10.1016\/j.pneurobio.2023.102462","DOI":"10.1016\/j.pneurobio.2023.102462"},{"key":"5883_CR23","doi-asserted-by":"publisher","unstructured":"Hong CT, Chen KY, Wang W, Chiu JY, Wu D, Chao TY, Hu CJ, Chau KD et al (2020) Insulin resistance promotes Parkinson\u2019s disease through aberrant expression of \u03b1-synuclein, mitochondrial dysfunction, and deregulation of the polo-like kinase 2 signaling. Cells. https:\/\/doi.org\/10.3390\/cells9030740","DOI":"10.3390\/cells9030740"},{"issue":"20","key":"5883_CR24","doi-asserted-by":"publisher","first-page":"e2303","DOI":"10.1212\/WNL.0000000000201202","volume":"99","author":"K Cousins","year":"2022","unstructured":"Cousins K, Arezoumandan S, Shellikeri S, Ohm D, Shaw LM, Grossman M, Wolk D, McMillan CT et al (2022) CSF biomarkers of Alzheimer disease in patients with concomitant \u03b1-synuclein pathology. Neurology 99(20):e2303\u2013e2312. https:\/\/doi.org\/10.1212\/WNL.0000000000201202","journal-title":"Neurology"},{"issue":"2","key":"5883_CR25","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1111\/jnc.14719","volume":"150","author":"MS Garc\u00eda-Ayll\u00f3n","year":"2019","unstructured":"Garc\u00eda-Ayll\u00f3n MS, Monge-Argil\u00e9s JA, Monge-Garc\u00eda V, Navarrete F, Cort\u00e9s-G\u00f3mez MA, S\u00e1nchez-Pay\u00e1 J, Manzanares J, Gasparini-Berenguer R et al (2019) Measurement of CSF \u03b1-synuclein improves early differential diagnosis of mild cognitive impairment due to Alzheimer\u2019s disease. J Neurochem 150(2):218\u2013230. https:\/\/doi.org\/10.1111\/jnc.14719","journal-title":"J Neurochem"},{"issue":"1","key":"5883_CR26","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","volume":"23","author":"JG Greener","year":"2022","unstructured":"Greener JG, Kandathil SM, Moffat L, Jones DT (2022) A guide to machine learning for biologists. Nat Rev Mol Cell Biol 23(1):40\u201355. https:\/\/doi.org\/10.1038\/s41580-021-00407-0","journal-title":"Nat Rev Mol Cell Biol"},{"issue":"1","key":"5883_CR27","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-78120-z","volume":"14","author":"M Joddrell","year":"2024","unstructured":"Joddrell M, El-Bouri W, Harrison SL, Huisman MV, Lip G, Zheng Y, Lip GYH (2024) Machine learning for outcome prediction in patients with non-valvular atrial fibrillation from the GLORIA-AF registry. Sci Rep 14(1):27088. https:\/\/doi.org\/10.1038\/s41598-024-78120-z","journal-title":"Sci Rep"},{"key":"5883_CR28","doi-asserted-by":"publisher","first-page":"4425","DOI":"10.2147\/DMSO.S489819","volume":"17","author":"Z Xia","year":"2024","unstructured":"Xia Z, Cao S, Li T, Qin Y, Zhong Y (2024) Risk prediction models for mild cognitive impairment in patients with type 2 diabetes mellitus: a systematic review and meta-analysis. Diabetes Metab Syndr Obes 17:4425\u20134438. https:\/\/doi.org\/10.2147\/DMSO.S489819","journal-title":"Diabetes Metab Syndr Obes"},{"issue":"5","key":"5883_CR29","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","volume":"16","author":"G Yu","year":"2012","unstructured":"Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS A J Integr Biol 16(5):284\u2013287. https:\/\/doi.org\/10.1089\/omi.2011.0118","journal-title":"OMICS A J Integr Biol"},{"key":"5883_CR30","doi-asserted-by":"publisher","unstructured":"Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607\u2013D613. https:\/\/doi.org\/10.1093\/nar\/gky1131","DOI":"10.1093\/nar\/gky1131"},{"key":"5883_CR31","doi-asserted-by":"publisher","unstructured":"Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498\u20132504. https:\/\/doi.org\/10.1101\/gr.1239303","DOI":"10.1101\/gr.1239303"},{"issue":"Suppl 4","key":"5883_CR32","doi-asserted-by":"publisher","DOI":"10.1186\/1752-0509-8-S4-S11","volume":"8","author":"CH Chin","year":"2014","unstructured":"Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(Suppl 4):S11. https:\/\/doi.org\/10.1186\/1752-0509-8-S4-S11","journal-title":"BMC Syst Biol"},{"key":"5883_CR33","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compbiolchem.2019.01.010","volume":"79","author":"MK Gupta","year":"2019","unstructured":"Gupta MK, Vadde R (2019) Identification and characterization of differentially expressed genes in type 2 diabetes using in silico approach. Comput Biol Chem 79:24\u201335. https:\/\/doi.org\/10.1016\/j.compbiolchem.2019.01.010","journal-title":"Comput Biol Chem"},{"issue":"1","key":"5883_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-74480-8","volume":"14","author":"Q Wang","year":"2024","unstructured":"Wang Q, Yang Y (2024) Bioinformatics analysis of effective biomarkers and immune infiltration in type 2 diabetes with cognitive impairment and aging. Sci Rep 14(1):23279. https:\/\/doi.org\/10.1038\/s41598-024-74480-8","journal-title":"Sci Rep"},{"issue":"3","key":"5883_CR35","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1111\/j.1365-2796.2004.01380.x","volume":"256","author":"B Winblad","year":"2004","unstructured":"Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund LO, Nordberg A, B\u00e4ckman L et al (2004) Mild cognitive impairment- -beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med 256(3):240\u2013246. https:\/\/doi.org\/10.1111\/j.1365-2796.2004.01380.x","journal-title":"J Intern Med"},{"issue":"4","key":"5883_CR36","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1111\/j.1532-5415.2005.53221.x","volume":"53","author":"ZS Nasreddine","year":"2005","unstructured":"Nasreddine ZS, Phillips NA, B\u00e9dirian V, Charbonneau S, Whitehead V, Collin I, Cummings JL, Chertkow H (2005) The Montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53(4):695\u2013699. https:\/\/doi.org\/10.1111\/j.1532-5415.2005.53221.x","journal-title":"J Am Geriatr Soc"},{"key":"5883_CR37","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m441","volume":"368","author":"RD Riley","year":"2020","unstructured":"Riley RD, Ensor J, Snell K, Harrell FE Jr, Martin GP, Reitsma JB, Moons K, Collins G et al (2020) Calculating the sample size required for developing a clinical prediction model. BMJ 368:m441. https:\/\/doi.org\/10.1136\/bmj.m441","journal-title":"BMJ"},{"issue":"15","key":"5883_CR38","doi-asserted-by":"publisher","first-page":"10192","DOI":"10.1002\/ece3.7826","volume":"11","author":"W Li","year":"2021","unstructured":"Li W, Guo Q (2021) Plotting receiver operating characteristic and precision-recall curves from presence and background data. Ecol Evol 11(15):10192\u201310206. https:\/\/doi.org\/10.1002\/ece3.7826","journal-title":"Ecol Evol"},{"issue":"1","key":"5883_CR39","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-021-00900-w","volume":"13","author":"S Grueso","year":"2021","unstructured":"Grueso S, Viejo-Sobera R (2021) Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer\u2019s disease dementia: a systematic review. Alzheimers Res Ther 13(1):162. https:\/\/doi.org\/10.1186\/s13195-021-00900-w","journal-title":"Alzheimers Res Ther"},{"key":"5883_CR40","doi-asserted-by":"publisher","unstructured":"Minta K, Brinkmalm G, Janelidze S, Sj\u00f6din S, Portelius E, Stomrud E, Zetterberg H, Blennow K et al (2020) Quantification of total apolipoprotein E and its isoforms in cerebrospinal fluid from patients with neurodegenerative diseases. Alzheimers Res Ther 12(1):19. https:\/\/doi.org\/10.1186\/s13195-020-00585-7","DOI":"10.1186\/s13195-020-00585-7"},{"issue":"8","key":"5883_CR41","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1016\/j.jalz.2017.06.2266","volume":"13","author":"V Ovod","year":"2017","unstructured":"Ovod V, Ramsey KN, Mawuenyega KG, Bollinger JG, Hicks T, Schneider T, Sullivan M, Paumier K et al (2017) Amyloid \u03b2 concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement 13(8):841\u2013849. https:\/\/doi.org\/10.1016\/j.jalz.2017.06.2266","journal-title":"Alzheimers Dement"},{"issue":"6","key":"5883_CR42","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.jalz.2019.03.009","volume":"15","author":"A Vergallo","year":"2019","unstructured":"Vergallo A, M\u00e9gret L, Lista S, Cavedo E, Zetterberg H, Blennow K, Vanmechelen E, De Vos A et al (2019) Plasma amyloid \u03b2 40\/42 ratio predicts cerebral amyloidosis in cognitively normal individuals at risk for Alzheimer\u2019s disease. Alzheimers Dement 15(6):764\u2013775. https:\/\/doi.org\/10.1016\/j.jalz.2019.03.009","journal-title":"Alzheimers Dement"},{"key":"5883_CR43","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.parkreldis.2022.06.001","volume":"99","author":"M Zubelzu","year":"2022","unstructured":"Zubelzu M, Morera-Herreras T, Irastorza G, G\u00f3mez-Esteban JC, Murueta-Goyena A (2022) Plasma and serum alpha-synuclein as a biomarker in Parkinson\u2019s disease: a meta-analysis. Parkinsonism Relat Disord 99:107\u2013115. https:\/\/doi.org\/10.1016\/j.parkreldis.2022.06.001","journal-title":"Parkinsonism Relat Disord"},{"issue":"4","key":"5883_CR44","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.14336\/AD.2019.0920","volume":"11","author":"Z Guo","year":"2020","unstructured":"Guo Z, Cao Q, Zhao Z, Song C (2020) Biogenesis, features, functions, and disease relationships of a specific circular RNA: CDR1as. Aging Dis 11(4):1009\u20131020. https:\/\/doi.org\/10.14336\/AD.2019.0920","journal-title":"Aging Dis"},{"key":"5883_CR45","doi-asserted-by":"publisher","unstructured":"Zhang Y, Sun L, Xuan L, Pan Z, Li K, Liu S, Huang Y, Zhao X et al (2016) Reciprocal changes of circulating long non-coding RNAs ZFAS1 and CDR1AS predict acute myocardial infarction. Sci Rep 6:22384. https:\/\/doi.org\/10.1038\/srep22384","DOI":"10.1038\/srep22384"},{"issue":"1","key":"5883_CR46","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1186\/s13018-020-02129-z","volume":"15","author":"B Jiang","year":"2020","unstructured":"Jiang B, Zhu SH, Zeng JY, Mao Z (2020) Plasma and local expressions of CircRNA CDR1as are linked with disease severity in patients with non-traumatic osteonecrosis of femoral head. J Orthop Surg Res 15(1):592. https:\/\/doi.org\/10.1186\/s13018-020-02129-z","journal-title":"J Orthop Surg Res"},{"issue":"1","key":"5883_CR47","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1186\/s40001-024-02015-y","volume":"29","author":"C Yuan","year":"2024","unstructured":"Yuan C, Xu Y, Zhou L, Peng J, Sha R, Lin Y, Xu S, Ye Y et al (2024) Value of CDR1-AS as a predictive and prognostic biomarker for patients with breast cancer receiving neoadjuvant chemotherapy in a prospective Chinese cohort. Eur J Med Res 29(1):454. https:\/\/doi.org\/10.1186\/s40001-024-02015-y","journal-title":"Eur J Med Res"},{"issue":"11","key":"5883_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2023.107354","volume":"32","author":"W Ji","year":"2023","unstructured":"Ji W, Wang C, Chen H, Liang Y, Wang S (2023) Predicting post-stroke cognitive impairment using machine learning: a prospective cohort study. J Stroke Cerebrovasc Dis 32(11):107354. https:\/\/doi.org\/10.1016\/j.jstrokecerebrovasdis.2023.107354","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"5883_CR49","doi-asserted-by":"publisher","unstructured":"Lee M, Yeo NY, Ahn HJ, Lim JS, Kim Y, Lee SH, Oh MS, Lee BC et al (2023) Prediction of post-stroke cognitive impairment after acute ischemic stroke using machine learning. Alzheimers Res Ther 15(1):147. https:\/\/doi.org\/10.1186\/s13195-023-01289-4","DOI":"10.1186\/s13195-023-01289-4"},{"issue":"37","key":"5883_CR50","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aay7120","volume":"4","author":"D Gunning","year":"2019","unstructured":"Gunning D, Stefik M, Choi J, Miller T, Stumpf S, Yang GZ (2019) XAI-explainable artificial intelligence. Sci Robot 4(37):eaay7120. https:\/\/doi.org\/10.1126\/scirobotics.aay7120","journal-title":"Sci Robot"},{"key":"5883_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neulet.2022.136908","volume":"791","author":"S Xia","year":"2022","unstructured":"Xia S, Zhang Y, Peng B, Hu X, Zhou L, Chen C, Lu C, Chen M et al (2022) Detection of mild cognitive impairment in type 2 diabetes mellitus based on machine learning using privileged information. Neurosci Lett 791:136908. https:\/\/doi.org\/10.1016\/j.neulet.2022.136908","journal-title":"Neurosci Lett"},{"key":"5883_CR52","doi-asserted-by":"publisher","unstructured":"Chen Y, Zhou Z, Liang Y, Tan X, Li Y, Qin C, Feng Y, Ma X et al (2021) Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity. Hum Brain Mapp 42(14):4671\u20134684. https:\/\/doi.org\/10.1002\/hbm.25575","DOI":"10.1002\/hbm.25575"},{"issue":"4","key":"5883_CR53","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.gpb.2019.09.002","volume":"17","author":"Z Liu","year":"2019","unstructured":"Liu Z, Liu J, Yuan H, Liu T, Cui X, Tang Z, Du Y, Wang M et al (2019) Identification of cognitive dysfunction in patients with T2DM using whole brain functional connectivity. Genomics Proteomics Bioinformatics 17(4):441\u2013452. https:\/\/doi.org\/10.1016\/j.gpb.2019.09.002","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"5883_CR54","doi-asserted-by":"publisher","unstructured":"Yu H, Gao Y, He T, Li M, Zhang Y, Zheng J, Jiang B, Chen C et al (2022) Discovering new peripheral plasma biomarkers to identify cognitive decline in type 2 diabetes. Front Cell Dev Biol 10:818141. https:\/\/doi.org\/10.3389\/fcell.2022.818141","DOI":"10.3389\/fcell.2022.818141"},{"key":"5883_CR55","doi-asserted-by":"publisher","unstructured":"Maimaitituerxun R, Chen W, Xiang J, Xie Y, Xiao F, Wu XY, Chen L, Yang J et al (2024) Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: a CHAID decision tree analysis. Brain Behav 14(3):e3456. https:\/\/doi.org\/10.1002\/brb3.3456","DOI":"10.1002\/brb3.3456"},{"key":"5883_CR56","doi-asserted-by":"publisher","unstructured":"Xiang L, Lou J, Zhao J, Geng Y, Zhang J, Wu Y, Zhao Y, Tao Z et al (2025) Underlying mechanism of lysosomal membrane permeabilization in CNS injury: a literature review. Mol Neurobiol 62(1):626\u2013642. https:\/\/doi.org\/10.1007\/s12035-024-04290-6","DOI":"10.1007\/s12035-024-04290-6"},{"key":"5883_CR57","doi-asserted-by":"publisher","DOI":"10.4103\/NRR.NRR-D-25-00701","author":"H Yuan","year":"2025","unstructured":"Yuan H, Shi J, Gu C, Yuan J, Huang C, Li X, Zhou K, Qi J (2025) Akkermansia muciniphila: a next-generation gut probiotic supporting neurorepair and functional recovery. Neural Regen Res. https:\/\/doi.org\/10.4103\/NRR.NRR-D-25-00701","journal-title":"Neural Regen Res"},{"key":"5883_CR58","doi-asserted-by":"publisher","unstructured":"Zhang H, Ni W, Yu G, Geng Y, Lou J, Qi J, Chen Y, Li F et al (2023) 3,4-Dimethoxychalcone, a caloric restriction mimetic, enhances TFEB-mediated autophagy and alleviates pyroptosis and necroptosis after spinal cord injury. Theranostics 13(2):810\u2013832. https:\/\/doi.org\/10.7150\/thno.78370","DOI":"10.7150\/thno.78370"}],"container-title":["Molecular Neurobiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12035-026-05883-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12035-026-05883-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12035-026-05883-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:05:31Z","timestamp":1777248331000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12035-026-05883-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":58,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["5883"],"URL":"https:\/\/doi.org\/10.1007\/s12035-026-05883-z","relation":{},"ISSN":["0893-7648","1559-1182"],"issn-type":[{"value":"0893-7648","type":"print"},{"value":"1559-1182","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"11 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was conducted in strict adherence to the ethical principles of the 1964 Declaration of Helsinki. We confirm that the study was approved by the Research Ethics Committee at the Affiliated Zhongda Hospital of Southeast University (approval no. of ethics committee: 2021ZDSYLL435-Y01), the study (Trial registry: Chinese Clinical Trial Registry; Trial registration number: ChiCTR-OCC-15006060; Registration date: 2017), China. Informed consent was obtained from all individual participants included in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"588"}}