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Juan Górriz Sáez

, University of Granada

Email: info@benthamscience.net

参考

  1. Willoughby-Dudley, K.A.; Darwin, M.L.; D.B. Davalos The postoperative effects of anesthesia exposure on cognitive decline in older adults: A narrative review. Curr. Alzheimer Res., 2024, 21(1), 3-23.
  2. Kalyaanamoorthy, S.; Opare, K.; Xu, X.; Ganesan, A.; Rao, P.P.N. Post-translational modifications in tau and their roles in Alzheimer’s pathology. Curr. Alzheimer Res., 2024, 21(1), 24-49.
  3. Nagori, K.; Pradhan, M.; Sharma, M. Ajazuddin, Badwaik HR, Nakhate KT. Current progress on central cholinergic receptors as therapeutic targets for Alzheimer’s disease. Curr. Alzheimer Res., 2024, 21(1), 50-68.
  4. Padala, S.; Setti, S.; Raymick, J.; Hanig, J.; Sarkar, S. Evaluation and characterization of modified K114 method to localize plaques in rodent and plaques and tangles in human brain tissue. Curr. Alzheimer Res., 2024, 21(1), 69-80.
  5. Gajowniczek-Ałasa, D.; Szwajgier, D.; Baranowska-Wójcik, E. Plant soup formulations show cholinesterase inhibition potential in the prevention of Alzheimer’s disease. Curr. Alzheimer Res., 2024, 21(1), 81-89.
  6. Five biomarkers from one cerebrospinal fluid sample to stage Alzheimer’s disease. Nat Aging, 2024. doi: 10.1038/s43587-024-00618-y
  7. Bueichekú, E.; Diez, I.; Gagliardi, G. Multi-modal neuroimaging phenotyping of mnemonic anosognosia in the aging brain. Commun. Med., 2024, 4, 65. doi: 10.1038/s43856-024-00497-9
  8. Gorriz, J.M.; Segovia, F.; Ramirez, J.; Ortiz, A.; Suckling, J. Is K-fold cross validation the best model selection method for Machine Learning? arXiv preprint arXiv:2401.16407., doi: 10.1038/d41586-023-03482-9
  9. Iankova, V.; Sparber, P.; Rohani, M.; Dusek, P.; Büchner, B.; Karin, I. Phenotype and natural history of mitochondrial membrane protein- associated neurodegeneration. Brain, 2024, 147(4), 1389-1398.
  10. Górriz, J.M.; Álvarez-Illán, I.; Álvarez-Marquina, A; Arco, JE; Atzmueller Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends. Information Fusion, 2023, 100, 101945.
  11. Jimenez- Mesa, C; Arco, JE; Martinez-Murcia, FJ; Suckling, J; Ramirez, J. Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects. Pharmacol. Res., 2023, 197, 106984.

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