Prasad, K. & Lee, P. Suppression of hypercholesterolemic atherosclerosis by pentoxifylline and its mechanism. Atherosclerosis 192(2), 313–322. https://doi.org/10.1016/j.atherosclerosis.2006.07.034 (2007).
Google Scholar
Martin, S. S. et al. 2024 Heart disease and stroke statistics: A report of US and global data from the American heart association. Circulation 149, e347–e913. https://doi.org/10.1161/CIR.0000000000001209 (2024).
Google Scholar
Carbonell, T. & Gomes, A. V. MicroRNAs in the regulation of cellular redox status and its implications in myocardial ischemia-reperfusion injury. Redox Biol. 36, 101607. https://doi.org/10.1016/j.redox.2020.101607 (2020).
Google Scholar
Zhang, M. et al. Ischemia-reperfusion injury: Molecular mechanisms and therapeutic targets. Signal Transduct. Target. Ther. 9, 12. https://doi.org/10.1038/s41392-023-01688-x (2024).
Google Scholar
Zhang, S. et al. The pathological mechanisms and potential therapeutic drugs for myocardial ischemia reperfusion injury. Phytomed.: Int. J. Phytother Phytopharmacol. 129, 155649. https://doi.org/10.1016/j.phymed.2024.155649 (2024).
Google Scholar
Toldo, S. et al. The NLRP3 inflammasome inhibitor, OLT1177 (dapansutrile), reduces infarct size and preserves contractile function after ischemia reperfusion injury in the mouse. J. Cardiovasc. Pharmacol. 73, 215–222. https://doi.org/10.1097/FJC.0000000000000658 (2019).
Google Scholar
Francisco, J. & Del Re, D. P. Inflammation in myocardial ischemia/reperfusion injury: underlying mechanisms and therapeutic potential. Antioxidants (Basel, Switzerland) 12, 1944. https://doi.org/10.3390/antiox12111944 (2023).
Google Scholar
Goncharov, R. G. & Sharapov, M. G. Mol. Biol. 57, 1150–1174. https://doi.org/10.1134/S0026893323060067 (2023).
Google Scholar
McKinsey, T. A. et al. Emerging epigenetic therapies of cardiac fibrosis and remodelling in heart failure: from basic mechanisms to early clinical development. Cardiovasc. Res. 118, 3482–3498. https://doi.org/10.1093/cvr/cvac142 (2023).
Google Scholar
Mansoor, S., Hamid, S., Tuan, T. T., Park, J. E. & Chung, Y. S. Advance computational tools for multiomics data learning. Biotechnol. Adv. 77, 108447. https://doi.org/10.1016/j.biotechadv.2024.108447 (2024).
Google Scholar
Jayawardena, E., Medzikovic, L., Ruffenach, G. & Eghbali, M. Role of miRNA-1 and miRNA-21 in acute myocardial ischemia-reperfusion injury and their potential as therapeutic strategy. Int. J. Mol. Sci. 23, 1512. https://doi.org/10.3390/ijms23031512 (2022).
Google Scholar
Liu, K. et al. Identification of microRNAs related to myocardial ischemic reperfusion injury. J. Cell. Physiol. 234, 11380–11390. https://doi.org/10.1002/jcp.27795 (2019).
Google Scholar
Wu, X., Zhu, H., Zhu, S., Hao, M. & Li, Q. Identification of microRNAs related to myocardial ischemic reperfusion injury. J. Cell. Physiol. 234, 11380–11390. https://doi.org/10.1002/jcp.27795 (2019).
Google Scholar
Liu, Y. et al. Expression profiling and ontology analysis of long noncoding RNAs in post-ischemic heart and their implied roles in ischemia/reperfusion injury. Gene 543, 15–21. https://doi.org/10.1016/j.gene.2014.04.016 (2014).
Google Scholar
Sun, Y. et al. Non-coding RNAs modulate pyroptosis in myocardial ischemia-reperfusion injury: A comprehensive review. Int. J. Biol. Macromol. 257, 128558. https://doi.org/10.1016/j.ijbiomac.2023.128558 (2024).
Google Scholar
Zhao, Z. et al. Long noncoding RNAs in myocardial ischemia-reperfusion injury. Oxid. Med. Cell. Longev. 2021, 8889123. https://doi.org/10.1155/2021/8889123 (2021).
Google Scholar
Thum, T. et al. MicroRNA-21 contributes to myocardial disease by stimulating MAP kinase signalling in fibroblasts. Nature 456, 980–984. https://doi.org/10.1038/nature07511 (2008).
Google Scholar
Yang, J. et al. MicroRNA-22 targeting CBP protects against myocardial ischemia-reperfusion injury through anti-apoptosis in rats. Mol. Biol. Rep. 41, 555–561. https://doi.org/10.1007/s11033-013-2891-x (2014).
Google Scholar
Seker, U. et al. Regulation of STAT3 and NF-κB signaling pathways by trans-Anethole in testicular ischemia-reperfusion injury and its gonadoprotective effect. Revista internacional de andrologia 22, 57–67. https://doi.org/10.22514/j.androl.2024.015 (2024).
Google Scholar
Torghabeh, F. D., Javadi, B. & Sahebkar, A. Dietary anethole: a systematic review of its protective effects against metabolic syndrome. J. Diabetes Metab. Disord. 23, 619–631. https://doi.org/10.1007/s40200-023-01322-1 (2023).
Google Scholar
Raposo, A. et al. Anethole in cancer therapy: Mechanisms, synergistic potential, and clinical challenges. Biomed. Pharmacother. Biomed. Pharmacother. 180, 117449. https://doi.org/10.1016/j.biopha.2024.117449 (2024).
Google Scholar
González-Espinoza, L. et al. Pentoxifylline decreases serum levels of tumor necrosis factor alpha, interleukin 6 and C-reactive protein in hemodialysis patients: Results of a randomized double-blind, controlled clinical trial. Nephrol., Dialysis, Transpl.: Off. Publ. Eur. Dialysis Transpl. Assoc. Eur. Renal Assoc. 27, 2023–2028. https://doi.org/10.1093/ndt/gfr579 (2012).
Google Scholar
Dhulqarnain, A. O. et al. Pentoxifylline improves the survival of spermatogenic cells via oxidative stress suppression and upregulation of PI3K/AKT pathway in mouse model of testicular torsion-detorsion. Heliyon 7, e06868. https://doi.org/10.1016/j.heliyon.2021.e06868 (2021).
Google Scholar
Baek, H., Sanjay, N., Park, M. & Lee, H. J. Cyanidin-3-O-glucoside protects the brain and improves cognitive function in APPswe/PS1ΔE9 transgenic mice model. J. Neuroinflamm. 20, 268. https://doi.org/10.1186/s12974-023-02950-3 (2023).
Google Scholar
Speciale, A. et al. Cyanidin-3-O-glucoside counters the response to TNF-alpha of endothelial cells by activating Nrf2 pathway. Mol. Nutr. Food Res. 57, 1979–1987. https://doi.org/10.1002/mnfr.201300102 (2013).
Google Scholar
Aleksova, A. et al. Biomarkers of Importance in Monitoring Heart Condition After Acute Myocardial Infarction. J. Clin. Med. 14(1), 129. https://doi.org/10.3390/jcm14010129 (2025).
Google Scholar
Abbas, N. A. & Kabil, S. L. Pentoxifylline and cilostazol against rat heart injuries induced by doxorubicin. Egypt. J. Basic Clin. Pharmacol. https://doi.org/10.11131/2017/101364 (2017).
Google Scholar
Vasan, R. S. Biomarkers of cardiovascular disease: Molecular basis and practical considerations. Circulation 113(19), 2335–2362 (2006).
Google Scholar
Li, C., Liu, Z. & Shi, R. A bibliometric analysis of 14,822 researches on myocardial reperfusion injury by machine learning. Int. J. Environ. Res. Public Health 18, 8231. https://doi.org/10.3390/ijerph18158231 (2021).
Google Scholar
Akbar, S., Ullah, M., Raza, A., Zou, Q. & Alghamdi, W. DeepAIPs-Pred: predicting anti-inflammatory peptides using local evolutionary transformation images and structural embedding-based optimal descriptors with Self-Normalized BiTCNs. J. Chem. Inf. Model. 64(24), 9609–9625 (2024).
Google Scholar
Shahid, M. et al. pACP-HybDeep: Predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning. Sci. Rep. 15(1), 565 (2025).
Google Scholar
Akbar, S. et al. pNPs-CapsNet: predicting neuropeptides using protein language models and FastText encoding-based weighted multi-view feature integration with deep capsule neural network. ACS Omega 10(12), 12403–12416 (2025).
Google Scholar
Mehmood, A., Kaushik, A. C. & Wei, D. Q. DDSBC: A stacking ensemble classifier-based approach for breast cancer drug-pair cell synergy prediction. J. Chem. Inf. Model. 64(16), 6421–6431 (2024).
Google Scholar
Baczkó, I., Leprán, I. & Papp, J. G. Influence of Anesthetics on the Incidence of Reperfusion-Induced Arrhythmias and Sudden Death in Rats. J. Cardiovasc. Pharmacol. 29(2), 196–201 (1997).
Google Scholar
Wang, S. et al. Febuxostat pretreatment attenuates myocardial ischemia/reperfusion injury via mitochondrial apoptosis. J. Transl. Med. 13, 209. https://doi.org/10.1186/s12967-015-0578-x (2015).
Google Scholar
Wu, Y., Yin, X., Wijaya, C., Huang, M. H. & McConnell, B. K. Acute myocardial infarction in rats. J. Vis. Exp. 48, 2464 (2011).
Pacher, P., Nagayama, T., Mukhopadhyay, P., Bátkai, S. & Kass, D. A. Measurement of cardiac function using pressure-volume conductance catheter technique in mice and rats. Nat. Protoc. 3(9), 1422–1434 (2008).
Google Scholar
Kim, K. et al. Cardiac hypertrophy working group of the predictive safety testing consortium. Evaluation of cardiac toxicity biomarkers in rats from different laboratories. Toxicol. Pathol. 44(8), 1072–1083. https://doi.org/10.1177/0192623316668276 (2016).
Google Scholar
Zhou, Y., Tian, Z. & Zhang, Y. Protective effects of [compound] on myocardial injury in rats: Histopathological analysis using H&E staining. J. Cardiovasc. Pharmacol. 65(2), 123–130 (2015).
Krenning, G., Zeisberg, E. M. & Kalluri, R. The origin of fibroblasts and mechanism of cardiac fibrosis. J. Cell. Physiol. 225(3), 631–637 (2010).
Google Scholar
Papoutsidakis, N. et al. Early myocardial injury is an integral component of experimental acute liver failure–a study in two porcine models. Arch. Med. Sci.: AMS 7(2), 217 (2011).
Google Scholar
Edgar, R., Domrachev, M. & Lash, A. E. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30(1), 207–210 (2002).
Google Scholar
Stelzer, G. et al. GeneCards Version 3: The human gene integrator. Database (Oxford) 2016, baw030. https://doi.org/10.1093/database/baw030 (2016).
Google Scholar
Buraschi, S., Neill, T. & Iozzo, R. V. Decorin is a devouring proteoglycan: Remodeling of intracellular catabolism via autophagy and mitophagy. Matrix Biol.: J. Int. Soc. Matrix Biol. 75–76, 260–270. https://doi.org/10.1016/j.matbio.2017.10.005 (2019).
Google Scholar
Shi, Y. et al. Transcription factor SOX5 promotes the migration and invasion of fibroblast-like synoviocytes in part by regulating MMP-9 expression in collagen-induced arthritis. Front Immunol. 12(9), 749. https://doi.org/10.3389/fimmu.2018.00749.PMID:29706965;PMCID:PMC5906798 (2018).
Google Scholar
Zhang, Y., Zhang, Y., Song, Q., Wang, Y. & Pan, J. The role of Vav3 expression for inflammation and cell death during experimental myocardial infarction. Clinics (Sao Paulo, Brazil) 78, 100273. https://doi.org/10.1016/j.clinsp.2023.100273 (2023).
Google Scholar
Zhang, T. W. et al. Decorin inhibits nucleus pulposus apoptosis by matrix-induced autophagy via the mTOR pathway. J. Orthop. Res.: Off. Publ. Orthop. Res. Soc. 39(8), 1777–1788. https://doi.org/10.1002/jor.24882 (2021).
Google Scholar
Autophagy M. Distinct Roles of Autophagy in the Heart During Ischemia and Reperfusion.
Toldo, S. & Abbate, A. The NLRP3 inflammasome in acute myocardial infarction. Nat. Rev. Cardiol. 15(4), 203–214 (2018).
Google Scholar
Miranda, K. C. et al. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126(6), 1203–1217. https://doi.org/10.1016/j.cell.2006.07.031 (2006).
Google Scholar
Sticht, C., De La Torre, C., Parveen, A. & Gretz, N. miRWalk: An online resource for prediction of microRNA binding sites. PLoS ONE 113(10), e0206239 (2018).
Google Scholar
Mehmood, A., Li, R., Kaushik, A. C. & Wei, D. Q. Comparative analysis of the genomic and expression profiles of ANLN and KDR as prognostic markers in breast cancer. Silico Pharmacol. 13(1), 1–6 (2025).
Google Scholar
Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods (San Diego, Calif) 25, 402–408. https://doi.org/10.1006/meth.2001.1262 (2001).
Google Scholar
Gurvich, V. & Naumova, M. Logical contradictions in the one-way ANOVA and Tukey-Kramer multiple comparisons tests with more than two groups of observations. Symmetry 13(8), 1387. https://doi.org/10.3390/sym13081387 (2021).
Google Scholar
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Google Scholar
McKinney, W. Data structures for statistical computing in python. in Proceedings of the 9th Python in Science Conference 51–56 (2010).
Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar
Lemaître, G., Nogueira, F. & Aridas, C. K. Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(1), 559–563 (2017).
Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9(03), 90–95 (2007).
Google Scholar
Seabold, S. & Josef, P. statsmodels: Econometric and statistical modeling with python. in Proceedings of the 9th Python in Science Conference (2010).
Chicco, D. & Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6. https://doi.org/10.1186/s12864-019-6413-7 (2020).
Google Scholar
Chicco, D. & Jurman, G. The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Mining 16, 4. https://doi.org/10.1186/s13040-023-00322-4 (2023).
Google Scholar
Feng, L. et al. Simvastatin relieves myocardial ischemia/reperfusion injury in rats through hedgehog signaling pathway. Eur. Rev. Med. Pharmacol. Sci. 24, 6400–6408. https://doi.org/10.26355/eurrev_202006_21538 (2020).
Google Scholar
Wang, Y., Wang, L., Li, J. H., Zhao, H. W. & Zhang, F. Z. Morphine alleviates myocardial ischemia/reperfusion injury in rats by inhibiting TLR4/NF-κB signaling pathway. Eur. Rev. Med. Pharmacol. Sci. 23, 8616–8624. https://doi.org/10.26355/eurrev_201910_19178 (2019).
Google Scholar
Zhang, J., Jiang, H., Liu, D. & Wang, G. Effects of dexmedetomidine on myocardial ischemia-reperfusion injury through PI3K-Akt-mTOR signaling pathway. Eur. Rev. Med. Pharmacol. Sci. 23(15), 6736–6743. https://doi.org/10.26355/eurrev_201908_18565 (2019).
Google Scholar
Liu, H., Guo, X., Chu, Y. & Lu, S. Heart protective effects and mechanism of quercetin preconditioning on anti-myocardial ischemia reperfusion (IR) injuries in rats. Gene 545, 149–155. https://doi.org/10.1016/j.gene.2014.04.043 (2014).
Google Scholar
Matboli, M. et al. Anti-inflammatory effect of trans-anethol in a rat model of myocardial ischemia-reperfusion injury. Biomed. Pharmacother. Biomed. Pharmacother. 150, 113070. https://doi.org/10.1016/j.biopha.2022.113070 (2022).
Google Scholar
Khedr, M. et al. Cardioprotective Effect Of Cyanidin-3-O-Glucosidein Ischemic Heart Is Mediated Via Inhibition Of Autophagy. Egypt. J. Chem. 65, 179–186. https://doi.org/10.21608/EJCHEM.2021.95695.4494 (2022).
Google Scholar
Abdullah, M. O., El-Desouky, M. A., Matboly, M. S. & Elhakim, A. Anti-inflammatory influence of trans-anethole on the cardiac regenerative capacity in myocardial ischemia/reperfusion injuries. Egypt. J. Chem. 67, 309–321. https://doi.org/10.21608/EJCHEM.2023.235111.8585 (2024).
Google Scholar
Younis, N. S. & Mohamed, M. E. Anethole’s effects against myocardial infarction: The role of TLR4/NFκB and Nrf2/HO1 pathways. Chem. Biol. Interact. 360, 109947. https://doi.org/10.1016/j.cbi.2022.109947 (2022).
Google Scholar
Zhang, C., Zhang, B., Chen, A., Yin, Q. & Wang, H. Trans-anethole attenuates diet-induced nonalcoholic steatohepatitis through suppressing TGF-β-mediated fibrosis. Clin. Res. Hepatol. Gastroenterol. 46, 101833. https://doi.org/10.1016/j.clinre.2021.101833 (2022).
Google Scholar
Kim, K. Y., Lee, H. S. & Seol, G. H. Anti-inflammatory effects of trans-anethole in a mouse model of chronic obstructive pulmonary disease. Biomed. Pharmacother. Biomed. Pharmacother. 91, 925–930. https://doi.org/10.1016/j.biopha.2017.05.032 (2017).
Google Scholar
Seo, E., Kang, P. & Seol, G. H. Trans-anethole prevents hypertension induced by chronic exposure to both restraint stress and nicotine in rats. Biomed. Pharmacother. Biomed. Pharmacother. 102, 249–253. https://doi.org/10.1016/j.biopha.2018.03.081 (2018).
Google Scholar
Rhee, Y. H., Moon, J. H., Mo, J. H., Pham, T. & Chung, P. S. mTOR and ROS regulation by anethole on adipogenic differentiation in human mesenchymal stem cells. BMC Cell Biol. 19, 12. https://doi.org/10.1186/s12860-018-0163-2 (2018).
Google Scholar
Samadi-Noshahr, Z. et al. trans-Anethole attenuated renal injury and reduced expressions of angiotensin II receptor (AT1R) and TGF-β in streptozotocin-induced diabetic rats. Biochimie 185, 117–127. https://doi.org/10.1016/j.biochi.2021.03.011 (2021).
Google Scholar
Fawzy, M. A., Nasr, G., Ali, F. E. & Fathy, M. Quercetin potentiates the hepatoprotective effect of sildenafil and/or pentoxifylline against intrahepatic cholestasis: Role of Nrf2/ARE, TLR4/NF-κB, and NLRP3/IL-1β signaling pathways. Life Sci. 314, 121343. https://doi.org/10.1016/j.lfs.2022.121343 (2023).
Google Scholar
Elshazly, S. M., Mahmoud, A. A. & Barakat, W. Pentoxifylline abrogates cardiotoxicity induced by the administration of a single high dose or multiple low doses of doxorubicin in rats. Can. J. Physiol. Pharmacol. 94, 1170–1177. https://doi.org/10.1139/cjpp-2016-0115 (2016).
Google Scholar
Saeed, A., Farouk, M. M., Sabri, N. A., Saleh, M. A. & Ahmed, M. A. Effect of pentoxifylline on endothelial dysfunction, oxidative stress and inflammatory markers in STEMI patients. Future Sci. OA 10, FSO967. https://doi.org/10.2144/fsoa-2023-0266 (2024).
Google Scholar
Elseweidy, M. M., Ali, S. I., Shaheen, M. A., Abdelghafour, A. M. & Hammad, S. K. Vanillin and pentoxifylline ameliorate isoproterenol-induced myocardial injury in rats via the Akt/HIF-1α/VEGF signaling pathway. Food Funct. 14, 3067–3082. https://doi.org/10.1039/d2fo03570g (2023).
Google Scholar
Li, W. et al. Potential role of cyanidin 3-glucoside (C3G) in diabetic cardiomyopathy in diabetic rats: An in vivo approach. Saudi J. Biol. Sci. 25, 500–506. https://doi.org/10.1016/j.sjbs.2016.11.007 (2018).
Google Scholar
Zhang, X. & Qin, X. CTRP3/AMPK pathway plays a key role in the anti-hypertrophic effects of cyanidin-3-O-glucoside by inhibiting the inflammatory response. Adv. Clin. Exp. Med.: Off. Organ. Wroclaw Med. Univ. 33, 831–841. https://doi.org/10.17219/acem/172546 (2024).
Google Scholar
Škėmienė, K., Jablonskienė, G., Liobikas, J. & Borutaitė, V. Protecting the heart against ischemia/reperfusion-induced necrosis and apoptosis: the effect of anthocyanins. Medicina (Kaunas) 49, 84–88. https://doi.org/10.3390/medicina49020015 (2013).
Google Scholar
Fushimi, T., Oyama, S., Koizumi, R., Fujii, Y. & Osakabe, N. Impact of cyanidin 3-O-glucoside on rat micro-and systemic circulation, possibly thorough angiogenesis. J. Clin. Biochem. Nutr. 72, 132–138. https://doi.org/10.3164/jcbn.22-50 (2023).
Google Scholar
Wang, Z. et al. Cyanidin-3-O-glucoside attenuates endothelial cell dysfunction by modulating miR-204-5p/SIRT1-mediated inflammation and apoptosis. BioFactors (Oxford, England) 46, 803–812. https://doi.org/10.1002/biof.1660 (2020).
Google Scholar
Knight, D. R. et al. A novel sodium-hydrogen exchanger isoform-1 inhibitor, zoniporide, reduces ischemic myocardial injury in vitro and in vivo. J. Pharmacol. Exp. Ther. 297, 254–259. https://doi.org/10.1016/S0022-3565(24)29535-5 (2001).
Google Scholar
Schaller, S. et al. TRO40303, a new cardioprotective compound, inhibits mitochondrial permeability transition. J. Pharmacol. Exp. Ther. 333, 696–706. https://doi.org/10.1124/jpet.110.167486 (2010).
Google Scholar
Hombach, S. & Kretz, M. Non-coding RNAs: Classification, Biology and Functioning. Adv. Exp. Med. Biol. 937, 3–17. https://doi.org/10.1007/978-3-319-42059-2_1 (2016).
Google Scholar
Doi, M. et al. Time-dependent changes of decorin in the infarct zone after experimentally induced myocardial infarction in rats: Comparison with biglycan. Pathol. Res. Pract. 196, 23–33. https://doi.org/10.1016/S0344-0338(00)80018-7 (2000).
Google Scholar
Takemoto, S. et al. Increased expression of dermatopontin mRNA in the infarct zone of experimentally induced myocardial infarction in rats: comparison with decorin and type I collagen mRNAs. Basic Res. Cardiol. 97, 461–468. https://doi.org/10.1007/s00395-002-0371-x (2002).
Google Scholar
Zimmerman, S. D. et al. Time course of collagen and decorin changes in rat cardiac and skeletal muscle post-MI. Am. J. Physiol. Heart Circul. Physiol. 281, H1816–H1822. https://doi.org/10.1152/ajpheart.2001.281.4.H1816 (2001).
Google Scholar
Jing, L., Hua, X., Yuanna, D., Rukun, Z. & Junjun, M. Exosomal miR-499a-5p inhibits endometrial cancer growth and metastasis via targeting VAV3. Cancer Manag. Res. 12, 13541–13552. https://doi.org/10.2147/CMAR.S283747 (2020).
Google Scholar
Tan, B. et al. Inhibition of gastric cancer cell growth and invasion through siRNA-mediated knockdown of guanine nucleotide exchange factor Vav3. Tumour Biol.: J. Int. Soc. Oncodevel. Biol. Med. 35, 1481–1488. https://doi.org/10.1007/s13277-013-1204-2 (2014).
Google Scholar
Tsuboi, M. et al. Vav3 is linked to poor prognosis of pancreatic cancers and promotes the motility and invasiveness of pancreatic cancer cells. Pancreatol.: Off. J. Int. Assoc. Pancreatol. (IAP) 16, 905–916. https://doi.org/10.1016/j.pan.2016.07.002 (2016).
Google Scholar
Lai, T. et al. SOX5 controls the sequential generation of distinct corticofugal neuron subtypes. Neuron 57, 232–247. https://doi.org/10.1016/j.neuron.2007.12.023 (2008).
Google Scholar
Zhang, W. et al. Neuroprotective effects of SOX5 against ischemic stroke by regulating VEGF/PI3K/AKT pathway. Gene 767, 145148. https://doi.org/10.1016/j.gene.2020.145148 (2021).
Google Scholar
Matboli, M. et al. Pentoxifylline alleviated cardiac injury via modulating the cardiac expression of lncRNA-00654-miR-133a-SOX5 mRNA in the rat model of ischemia-reperfusion. Biomed. Pharmacother. Biomed. Pharmacother. 124, 109842. https://doi.org/10.1016/j.biopha.2020.109842 (2020).
Google Scholar
Ouyang, C., Huang, L., Ye, X., Ren, M. & Han, Z. Overexpression of miR-1298 attenuates myocardial ischemia-reperfusion injury by targeting PP2A. J. Thromb. Thrombolysis 53, 136–148. https://doi.org/10.1007/s11239-021-02540-1 (2022).
Google Scholar
Cardenas-Gonzalez, M. et al. Identification, confirmation, and replication of novel urinary MicroRNA biomarkers in lupus nephritis and diabetic nephropathy. Clin. Chem. 63, 1515–1526. https://doi.org/10.1373/clinchem.2017.274175 (2017).
Google Scholar
Bian, Y. et al. CircHelz activates NLRP3 inflammasome to promote myocardial injury by sponging miR-133a-3p in mouse ischemic heart. J. Mol. Cell. Cardiol. 158, 128–139. https://doi.org/10.1016/j.yjmcc.2021.05.010 (2021).
Google Scholar
Dakhlallah, D. et al. MicroRNA-133a engineered mesenchymal stem cells augment cardiac function and cell survival in the infarct heart. J. Cardiovasc. Pharmacol. 65, 241–251. https://doi.org/10.1097/FJC.0000000000000183 (2015).
Google Scholar
García, R. et al. Circulating levels of miR-133a predict the regression potential of left ventricular hypertrophy after valve replacement surgery in patients with aortic stenosis. J. Am. Heart Assoc. 2, e000211. https://doi.org/10.1161/JAHA.113.000211 (2013).
Google Scholar
Li, M. et al. A circular transcript of ncx1 gene mediates ischemic myocardial injury by targeting miR-133a-3p. Theranostics 8, 5855–5869. https://doi.org/10.7150/thno.27285 (2018).
Google Scholar
Wang, Z., Luo, W., Zhong, P., Feng, Y. & Wang, H. lncRNA HAGLR modulates myocardial ischemia-reperfusion injury in mice through regulating miR-133a-3p/MAPK1 axis. Open Med. (Warsaw, Poland) 17, 1299–1307. https://doi.org/10.1515/med-2022-0519 (2022).
Google Scholar
Xiao, Y., Zhao, J., Tuazon, J. P., Borlongan, C. V. & Yu, G. MicroRNA-133a and Myocardial Infarction. Cell Transplant. 28, 831–838. https://doi.org/10.1177/0963689719843806 (2019).
Google Scholar
Mohamed, S. M., Medhat, H., Keshk, S., Matboli, M. & Hassan, M. K. LINC00654–SOX5 mRNA-miRNA-133a compose new RNA panel for colorectal cancer (CRC): A potential diagnostic panel for CRC. Biochem. (Moscow) Suppl. Ser. B: Biomed. Chem. 18, 151–166. https://doi.org/10.1134/S199075082460016X (2024).
Google Scholar
Xu, W. et al. Circulating lncRNA SNHG11 as a novel biomarker for early diagnosis and prognosis of colorectal cancer. Int. J. Cancer 146, 2901–2912. https://doi.org/10.1002/ijc.32747 (2020).
Google Scholar
Bai, X. et al. Cuproptosis-related lncRNA signature as a prognostic tool and therapeutic target in diffuse large B cell lymphoma. Sci. Rep. 14, 12926. https://doi.org/10.1038/s41598-024-63433-w (2024).
Google Scholar
Liu, H. et al. Long non-coding RNAs as prognostic markers in human breast cancer. Oncotarget 7, 20584–20596. https://doi.org/10.18632/oncotarget.7828 (2016).
Google Scholar
Darcy, A. M., Louie, A. K. & Roberts, L. W. Machine learning and the profession of medicine. JAMA 315, 551–552. https://doi.org/10.1001/jama.2015.18421 (2016).
Google Scholar
Mahmoudi, E. et al. Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review. BMJ (Clin. Res. Ed.) 369, m958. https://doi.org/10.1136/bmj.m958 (2020).
Google Scholar
Beam, A. L. & Kohane, I. S. Big Data and Machine Learning in Health Care. JAMA 319, 1317–1318. https://doi.org/10.1001/jama.2017.18391 (2018).
Google Scholar
Loring, Z., Mehrotra, S. & Piccini, J. P. Machine learning in “big data”: handle with care. Europace : Eur. Pacing, Arrhythmias, Cardiac Electrophysiol.: J. Work. Groups Cardiac Pacing, Arrhythmias, Cardiac Cell. Electrophysiol. Eur. Soc. Cardiol. 21, 1284–1285. https://doi.org/10.1093/europace/euz130 (2019).
Google Scholar
Goldstein, B. A., Navar, A. M. & Carter, R. E. Moving beyond regression techniques in cardiovascular risk prediction: Applying machine learning to address analytic challenges. Eur. Heart J. 38, 1805–1814. https://doi.org/10.1093/eurheartj/ehw302 (2017).
Google Scholar
Hyland, S. L. et al. Early prediction of circulatory failure in the intensive care unit using machine learning. Nat. Med. 26, 364–373. https://doi.org/10.1038/s41591-020-0789-4 (2020).
Google Scholar
Than, M. P. et al. Machine learning to predict the likelihood of acute myocardial infarction. Circulation 140, 899–909. https://doi.org/10.1161/CIRCULATIONAHA.119.041980 (2019).
Google Scholar
Sengupta, P. P., Kulkarni, H. & Narula, J. Prediction of abnormal myocardial relaxation from signal processed surface ECG. J. Am. Coll. Cardiol. 71, 1650–1660. https://doi.org/10.1016/j.jacc.2018.02.024 (2018).
Google Scholar
Vadapalli, S., Abdelhalim, H., Zeeshan, S. & Ahmed, Z. Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Brief. Bioinform. 23, bba191. https://doi.org/10.1093/bib/bbac191 (2022).
Google Scholar
Rampášek, L., Hidru, D., Smirnov, P., Haibe-Kains, B. & Goldenberg, A. Dr.VAE: improving drug response prediction via modeling of drug perturbation effects. Bioinformatics (Oxford, England) 35, 3743–3751. https://doi.org/10.1093/bioinformatics/btz158 (2019).
Google Scholar
Yamanishi, Y., Pauwels, E. & Kotera, M. Drug side-effect prediction based on the integration of chemical and biological spaces. J. Chem. Inf. Model. 52, 3284–3292. https://doi.org/10.1021/ci2005548 (2012).
Google Scholar
Venkatesan, K. et al. Prediction of drug response using genomic signatures from the cancer cell line encyclopedia. Clin. Cancer Res. 16(19_Supplement), PR2–PR2. https://doi.org/10.1158/DIAG-10-PR2 (2010).
Google Scholar
Wang, Y., Fang, J. & Chen, S. Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties. Sci. Rep. 6, 32679. https://doi.org/10.1038/srep32679 (2016).
Google Scholar
Zhang, N. et al. Predicting anticancer drug responses using a dual-layer integrated cell line-drug network model. PLoS Comput. Biol. 11, e1004498. https://doi.org/10.1371/journal.pcbi.1004498 (2015).
Google Scholar
Costello, J. C. et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol. 32, 1202–1212. https://doi.org/10.1038/nbt.2877 (2014).
Google Scholar
Huang, H. H., Dai, J. G. & Liang, Y. Clinical drug response prediction by using a Lq penalized network-constrained logistic regression method. Cell. Physiol. Biochem.: Int. J. Exp. Cell. Physiol., Biochem., Pharmacol. 51, 2073–2084. https://doi.org/10.1159/000495826 (2018).
Google Scholar
Samadishadlou, M. et al. Unlocking the potential of microRNAs: machine learning identifies key biomarkers for myocardial infarction diagnosis. Cardiovasc. Diabetol. 22, 247. https://doi.org/10.1186/s12933-023-01957-7 (2023).
Google Scholar
Li, H. et al. Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients. Front. Cardiovasc. Med. 9, 1059543. https://doi.org/10.3389/fcvm.2022.1059543 (2023).
Google Scholar
Liu, A. B. et al. Global prevalence and disability-adjusted life years of hypertensive heart disease: A trend analysis from the Global Burden of Disease Study 2019. J. Glob. Health 14, 04172. https://doi.org/10.7189/jogh.14.04172 (2024).
Google Scholar
Liu, Y., Li, L., Wang, Z., Zhang, J. & Zhou, Z. Myocardial ischemia-reperfusion injury; molecular mechanisms and prevention. Microvasc. Res. 149, 104565. https://doi.org/10.1016/j.mvr.2023.104565 (2023).
Google Scholar
