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Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints
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Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Neurology".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 21519

Special Issue Editors

Assist. Prof. Patrizia Giannoni

E-Mail Website
Guest Editor
Department of Neuroscience, Nimes University, Chrome Unit, EA 7352 Nîmes, France
Interests: pharmacology; toxicology; inflammation; bacterial infections; neurovascular unit; multidisciplinary approaches to neurodegenerative diseases
Dr. Sylvie Claeysen

E-Mail Website
Guest Editor
IGF, Univ Montpellier, Montpellier, France
Interests: neurodegenerative diseases; serotonin; G-protein-coupled receptors; neuropharmacology; microbiota

Special Issue Information

Dear Colleagues,

Alzheimer’s disease (AD), the most abundant form of dementia, currently represents a harsh social and economic issue worldwide. Rarely triggered by genetic mutations, AD is most commonly associated with a variety of risk factors, including aging, ApoE protein, cardiovascular diseases, diabetes and factors generally connected with increased inflammation levels (e.g., Western diet and sedentary lifestyle). Only a few symptomatic treatments are currently available; although several disease modifying agents are being tested in preclinical and clinical trials. Due to the heterogeneity of AD pathological hallmarks and clinical manifestations, research efforts are now being directed toward multitarget approaches and ad personam therapies. Only a precise and comprehensive understanding of the mechanisms triggering, developing and aggravating AD will guarantee an adequate response to the compelling need for effective treatments. This Special Issue of the Journal of Clinical Medicine will report on the current trends in AD prevention and curative strategies, highlighting new targets of intervention as well as novel therapeutic approaches.

Assist. Prof. Patrizia Giannoni
Dr. Sylvie Claeysen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Alzheimer’s
  • therapeutic target
  • multitarget-directed ligands
  • inflammation
  • vasculature
  • microbiota
  • prevention
  • biomarkers
  • diet

Published Papers (5 papers)

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Research

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12 pages, 507 KiB  
Article
Alexithymia in Alzheimer’s Disease
by Eva Mª Arroyo-Anlló, Corinne Souchaud, Pierre Ingrand, Jorge Chamorro Sánchez, Alejandra Melero Ventola and Roger Gil
J. Clin. Med. 2021, 10(1), 44; https://doi.org/10.3390/jcm10010044 - 25 Dec 2020
Cited by 1 | Viewed by 2407
Abstract
Alexithymia is widely recognized as the inability to identify and express emotions. It is a construct which consists of four cognitive traits such as difficulty in identifying feelings, describing feelings to others, externally oriented thinking, and limited imaginative capacity. Several studies have linked [...] Read more.
Alexithymia is widely recognized as the inability to identify and express emotions. It is a construct which consists of four cognitive traits such as difficulty in identifying feelings, describing feelings to others, externally oriented thinking, and limited imaginative capacity. Several studies have linked alexithymia to cognitive functioning, observing greater alexithymia scores associated with poorer cognitive abilities. Despite Alzheimer’s disease (AD) being a neurodegenerative pathology characterized by cognitive troubles from the early stages, associated to behavioral and emotional disturbances, very few investigations have studied the alexithymia in AD. These studies have shown that alexithymia scores—assessed with Toronto Alexithymia Scale (TAS)—were greater in AD patients than healthy participants. The objective of the study was to investigate if the alexithymia was present in patients with mild AD. We hypothesized that the AD group would show more alexithymia features than the control group. We evaluated 54 subjects, including 27 patients diagnosed with mild AD and 27 normal healthy controls, using the Shalling Sifneos Psychosomatic Scale (SSPS-R) and a neuropsychological test battery. Using non-parametric statistical analyses—Wilcoxon and Mann–Whitney U tests—we observed that the SSPS-R scores were similar in the AD and control groups. All participants showed SSPS-R scores below to 10 points, which means no-alexithymia. We did not find significant correlations between SSPS-R scores and cognitive variables in both groups (p > 0.22), but we observed a negative association between name abilities and alexithymia, but it does not reach to significance (p = 0.07). However, a significant correlation between SSPS-R score and mood state, assessed using Zerssen Rating Scale, was found in both groups (p = 0.01). Because we did not find a significant difference in the alexithymia assessment between both subject groups, pot hoc analyses were computed for each item of the SSPS-R. We made comparisons of alexithymic responses percentages in each SSPS-R item between AD and control groups, using Fisher’s test. We observed that AD patients produced more alexithymic responses in some items of SSPS-R test than the control group, particularly about difficulties to find the words to describe feelings, as well as difficulties of imagination capacity and externally oriented thinking. The present results do not confirm our hypothesis and they do not support the results of previous studies revealing great alexithymia in AD. Full article
(This article belongs to the Special Issue Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints)
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14 pages, 2204 KiB  
Article
Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning
by Makrina Karaglani, Krystallia Gourlia, Ioannis Tsamardinos and Ekaterini Chatzaki
J. Clin. Med. 2020, 9(9), 3016; https://doi.org/10.3390/jcm9093016 - 18 Sep 2020
Cited by 33 | Viewed by 4750
Abstract
Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology [...] Read more.
Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery. Full article
(This article belongs to the Special Issue Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints)
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14 pages, 2459 KiB  
Article
A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects
by Gopi Battineni, Nalini Chintalapudi, Francesco Amenta and Enea Traini
J. Clin. Med. 2020, 9(7), 2146; https://doi.org/10.3390/jcm9072146 - 8 Jul 2020
Cited by 57 | Viewed by 4887
Abstract
Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great [...] Read more.
Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage. Full article
(This article belongs to the Special Issue Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints)
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Review

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23 pages, 840 KiB  
Review
Neuropathology of the Brainstem to Mechanistically Understand and to Treat Alzheimer’s Disease
by Ágoston Patthy, János Murai, János Hanics, Anna Pintér, Péter Zahola, Tomas G. M. Hökfelt, Tibor Harkany and Alán Alpár
J. Clin. Med. 2021, 10(8), 1555; https://doi.org/10.3390/jcm10081555 - 7 Apr 2021
Cited by 9 | Viewed by 4087
Abstract
Alzheimer’s disease (AD) is a devastating neurodegenerative disorder as yet without effective therapy. Symptoms of this disorder typically reflect cortical malfunction with local neurohistopathology, which biased investigators to search for focal triggers and molecular mechanisms. Cortex, however, receives massive afferents from caudal brain [...] Read more.
Alzheimer’s disease (AD) is a devastating neurodegenerative disorder as yet without effective therapy. Symptoms of this disorder typically reflect cortical malfunction with local neurohistopathology, which biased investigators to search for focal triggers and molecular mechanisms. Cortex, however, receives massive afferents from caudal brain structures, which do not only convey specific information but powerfully tune ensemble activity. Moreover, there is evidence that the start of AD is subcortical. The brainstem harbors monoamine systems, which establish a dense innervation in both allo- and neocortex. Monoaminergic synapses can co-release neuropeptides either by precisely terminating on cortical neurons or, when being “en passant”, can instigate local volume transmission. Especially due to its early damage, malfunction of the ascending monoaminergic system emerges as an early sign and possible trigger of AD. This review summarizes the involvement and cascaded impairment of brainstem monoaminergic neurons in AD and discusses cellular mechanisms that lead to their dysfunction. We highlight the significance and therapeutic challenges of transmitter co-release in ascending activating system, describe the role and changes of local connections and distant afferents of brainstem nuclei in AD, and summon the rapidly increasing diagnostic window during the last few years. Full article
(This article belongs to the Special Issue Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints)
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19 pages, 944 KiB  
Review
Diagnostic Biomarkers for Alzheimer’s Disease Using Non-Invasive Specimens
by Maria Paraskevaidi, David Allsop, Salman Karim, Francis L. Martin and StJohn Crean
J. Clin. Med. 2020, 9(6), 1673; https://doi.org/10.3390/jcm9061673 - 1 Jun 2020
Cited by 33 | Viewed by 4175
Abstract
Studies in the field of Alzheimer’s disease (AD) have shown the emergence of biomarkers in biologic fluids that hold great promise for the diagnosis of the disease. A diagnosis of AD at a presymptomatic or early stage may be the key for a [...] Read more.
Studies in the field of Alzheimer’s disease (AD) have shown the emergence of biomarkers in biologic fluids that hold great promise for the diagnosis of the disease. A diagnosis of AD at a presymptomatic or early stage may be the key for a successful treatment, with clinical trials currently investigating this. It is anticipated that preventative and therapeutic strategies may be stage-dependent, which means that they have a better chance of success at a very early stage—before critical neurons are lost. Several studies have been investigating the use of cerebrospinal fluid (CSF) and blood as clinical samples for the detection of AD with a number of established core markers, such as amyloid beta (Aβ), total tau (T-tau) and phosphorylated tau (P-tau), being at the center of clinical research interest. The use of oral samples—including saliva and buccal mucosal cells—falls under one of the least-investigated areas in AD diagnosis. Such samples have great potential to provide a completely non-invasive alternative to current CSF and blood sampling procedures. The present work is a thorough review of the results and analytical approaches, including proteomics, metabolomics, spectroscopy and microbiome analyses that have been used for the study and detection of AD using salivary samples and buccal cells. With a few exceptions, most of the studies utilizing oral samples were performed in small cohorts, which in combination with the existence of contradictory results render it difficult to come to a definitive conclusion on the value of oral markers. Proteins such as Aβ, T-tau and P-tau, as well as small metabolites, were detected in saliva and have shown some potential as future AD diagnostics. Future large-cohort studies and standardization of sample preparation and (pre-)analytical factors are necessary to determine the use of these non-invasive samples as a diagnostic tool for AD. Full article
(This article belongs to the Special Issue Addressing Alzheimer’s Disease: Blueprints and Therapeutic Hints)
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