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Constantinou C, Meliou K, Skouras A, Siafaka P, Christodoulou P. Liposomes against Alzheimer's Disease: Current Research and Future Prospects. Biomedicines 2024; 12:1519. [PMID: 39062092 PMCID: PMC11275096 DOI: 10.3390/biomedicines12071519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/01/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
Alzheimer's disease, the most common neurodegenerative disease, affects more than 60 million people worldwide, a number that is estimated to double by 2050. Alzheimer's disease is characterized by progressive memory loss, the impairment of behavior, and mood changes, as well as the disturbed daily routine of the patient. Although there are some active molecules that can be beneficial by halting the progression of the disease, the blood-brain barrier and other physiological barriers hinder their delivery and, consequently, the appropriate management of the disease. Therefore, drug delivery systems that effectively target and overcome the blood-brain barrier to reach the targeted brain area would improve treatment effectiveness. Liposomes are lipophilic carriers that consist of a phospholipid bilayer structure, simulating the physiological lipidic layer of the blood-brain barrier and enabling better delivery of the drug to the brain. Given that pure liposomes may have less targeting affinity than functionalized liposomes, modification with groups such as lactoferrin, poly(ethylene glycol), and transferrin may improve specificity. In this mini-review, we summarize the literature on the use of liposomes for the treatment of Alzheimer's disease, focusing on the functionalization moieties of liposomes. In addition, challenges in brain delivery are also discussed.
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Affiliation(s)
- Christiana Constantinou
- Department of Life Sciences, School of Sciences, Pharmacy Program, European University Cyprus, 2404 Nicosia, Cyprus; (C.C.); (K.M.); (P.S.)
| | - Katerina Meliou
- Department of Life Sciences, School of Sciences, Pharmacy Program, European University Cyprus, 2404 Nicosia, Cyprus; (C.C.); (K.M.); (P.S.)
| | - Athanasios Skouras
- Department of Nursing, Faculty of Health Sciences, Hellenic Mediterranean University, 71004 Heraklion, Crete, Greece;
| | - Panoraia Siafaka
- Department of Life Sciences, School of Sciences, Pharmacy Program, European University Cyprus, 2404 Nicosia, Cyprus; (C.C.); (K.M.); (P.S.)
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Dolci G, Cruciani F, Rahaman MA, Abrol A, Chen J, Fu Z, Galazzo IB, Menegaz G, Calhoun VD. An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease. ARXIV 2024:arXiv:2406.13292v1. [PMID: 38947922 PMCID: PMC11213156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Alzheimer's disease (AD) is the most prevalent form of dementia, affecting millions worldwide with a progressive decline in cognitive abilities. The AD continuum encompasses a prodormal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD (MCIc) or remain stable (MCInc). Understanding the underlying mechanisms of AD requires complementary analysis derived from different data sources, leading to the development of multimodal deep learning models. In this study, we leveraged structural and functional Magnetic Resonance Imaging (sMRI/fMRI) to investigate the disease-induced grey matter and functional network connectivity changes. Moreover, considering AD's strong genetic component, we introduce Single Nucleotide Polymorphisms (SNPs) as a third channel. Given such diverse inputs, missing one or more modalities is a typical concern of multimodal methods. We hence propose a novel deep learning based classification framework where generative module employing Cycle Generative Adversarial Networks (cGAN) was adopted to impute missing data within the latent space. Additionally, we adopted an Explainable Artificial Intelligence (XAI) method, Integrated Gradients (IG), to extract input features relevance, enhancing our understanding of the learned representations. Two critical tasks were addressed: AD detection and MCI conversion prediction. Experimental results showed that our framework was able to reach the state-of-the-art in the classification of CN vs AD reaching an average test accuracy of 0.926 ± 0.02. For the MCInc vs MCIc task, we achieved an average prediction accuracy of 0.711 ± 0.01 using the pre-trained model for CN and AD. The interpretability analysis revealed that the classification performance was led by significant grey matter modulations in cortical and subcortical brain areas well known for their association with AD. Moreover, impairments in sensory-motor and visual resting state network connectivity along the disease continuum, as well as mutations in SNPs defining biological processes linked to amyloid-beta and cholesterol formation clearance and regulation, were identified as contributors to the achieved performance. Overall, our integrative deep learning approach shows promise for AD detection and MCI prediction, while shading light on important biological insights.
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Affiliation(s)
- Giorgio Dolci
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Federica Cruciani
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Md Abdur Rahaman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | | | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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Chaudhary S, Roy A, Summers C, Zhornitsky S, Ahles T, Li CSR, Chao HH. Hypothalamic connectivities predict individual differences in ADT-elicited changes in working memory and quality of life in prostate cancer patients. Sci Rep 2022; 12:9567. [PMID: 35688928 PMCID: PMC9187668 DOI: 10.1038/s41598-022-13361-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 05/24/2022] [Indexed: 11/09/2022] Open
Abstract
Androgen deprivation therapy (ADT) has been associated with adverse effects on cognition. However, we currently lack understanding of the neurobiology and prognostic markers of these effects. Given that ADT acts via the hypothalamus-pituitary-gonadal axis, we assessed whether baseline hypothalamic resting state functional connectivity (rsFC) could predict changes in working memory and quality of life in prostate cancer patients following androgen deprivation. In a prospective observational study, 28 men with non-metastatic prostate cancer receiving ADT and 38 patients not receiving ADT (controls), matched in age, years of education and Montreal Cognitive Assessment score, participated in brain imaging at baseline, and N-back task and quality-of-life (QoL) assessments at baseline and at 6 months follow-up. Imaging data were processed with published routines and evaluated at a corrected threshold. ADT and control groups did not differ in N-back performance or QoL across time points. In ADT, the changes in 0-back correct response rate (follow-up-baseline) were correlated with baseline hypothalamus-precentral gyrus rsFC; the changes in 1-back correct response rate and reaction time were each correlated with hypothalamus-middle frontal gyrus and superior parietal lobule rsFC. The changes in physical well-being subscore of QoL were correlated with baseline hypothalamus-anterior cingulate and cuneus rsFC. The hypothalamus rsFCs predicted N-back and QoL change with an area under the receiver operating characteristic curve of 0.93 and 0.73, respectively. Baseline hypothalamus-frontoparietal and salience network rsFC's predict inter-subject variations in the changes in working-memory and QoL following 6 months of ADT. Whether and how hypothalamic rsFCs may predict the cognitive and QoL effects with longer-term ADT remain to be investigated.
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Affiliation(s)
- Shefali Chaudhary
- Department of Psychiatry, Yale University School of Medicine, CMHC S110, 34 Park Street, New Haven, CT, 06519, USA.
| | - Alicia Roy
- Cancer Center, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, 06516, USA
| | - Christine Summers
- Cancer Center, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, 06516, USA
| | - Simon Zhornitsky
- Department of Psychiatry, Yale University School of Medicine, CMHC S110, 34 Park Street, New Haven, CT, 06519, USA
| | - Tim Ahles
- Department of Psychiatry and Behavioral Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University, New Haven, CT, 06520, USA
- Department of Neuroscience, Yale University, New Haven, CT, 06520, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, Yale University, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
| | - Herta H Chao
- Cancer Center, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, 06516, USA.
- Department of Medicine and Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, 06519, USA.
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