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Pradeepkiran JA, Baig J, Islam MA, Kshirsagar S, Reddy PH. Amyloid-β and Phosphorylated Tau are the Key Biomarkers and Predictors of Alzheimer's Disease. Aging Dis 2024:AD.2024.0286. [PMID: 38739937 DOI: 10.14336/ad.2024.0286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024] Open
Abstract
Alzheimer's disease (AD) is a age-related neurodegenerative disease and is a major public health concern both in Texas, US and Worldwide. This neurodegenerative disease is mainly characterized by amyloid-beta (Aβ) and phosphorylated Tau (p-Tau) accumulation in the brains of patients with AD and increasing evidence suggests that these are key biomarkers in AD. Both Aβ and p-tau can be detected through various imaging techniques (such as positron emission tomography, PET) and cerebrospinal fluid (CSF) analysis. The presence of these biomarkers in individuals, who are asymptomatic or have mild cognitive impairment can indicate an increased risk of developing AD in the future. Furthermore, the combination of Aβ and p-tau biomarkers is often used for more accurate diagnosis and prediction of AD progression. Along with AD being a neurodegenerative disease, it is associated with other chronic conditions such as cardiovascular disease, obesity, depression, and diabetes because studies have shown that these comorbid conditions make people more vulnerable to AD. In the first part of this review, we discuss that biofluid-based biomarkers such as Aβ, p-Tau in cerebrospinal fluid (CSF) and Aβ & p-Tau in plasma could be used as an alternative sensitive technique to diagnose AD. In the second part, we discuss the underlying molecular mechanisms of chronic conditions linked with AD and how they affect the patients in clinical care.
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Affiliation(s)
| | - Javaria Baig
- Internal Medicine Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Md Ariful Islam
- Internal Medicine Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Sudhir Kshirsagar
- Internal Medicine Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - P Hemachandra Reddy
- Internal Medicine Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Pharmacology & Neuroscience Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Neurology Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Speech, Language and Hearing Sciences Departments, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Public Health Department, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
- Nutritional Sciences Department, College of Human Sciences, Texas Tech University, Lubbock, TX 79409, USA
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Saunders S, Haider F, Ritchie CW, Muniz Terrera G, Luz S. Longitudinal observational cohort study: Speech for Intelligent cognition change tracking and DEtection of Alzheimer's Disease (SIDE-AD). BMJ Open 2024; 14:e082388. [PMID: 38548356 PMCID: PMC10982798 DOI: 10.1136/bmjopen-2023-082388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 02/19/2024] [Indexed: 04/02/2024] Open
Abstract
INTRODUCTION There is emerging evidence that speech may be a potential indicator and manifestation of early Alzheimer's disease (AD) pathology. Therefore, the University of Edinburgh and Sony Research have partnered to create the Speech for Intelligent cognition change tracking and DEtection of Alzheimer's Disease (SIDE-AD) study, which aims to develop digital speech-based biomarkers for use in neurodegenerative disease. METHODS AND ANALYSIS SIDE-AD is an observational longitudinal study, collecting samples of spontaneous speech. Participants are recruited from existing cohort studies as well as from the National Health Service (NHS)memory clinics in Scotland. Using an online platform, participants record a voice sample talking about their brain health and rate their mood, anxiety and apathy. The speech biomarkers will be analysed longitudinally, and we will use machine learning and natural language processing technology to automate the assessment of the respondents' speech patterns. ETHICS AND DISSEMINATION The SIDE-AD study has been approved by the NHS Research Ethics Committee (REC reference: 23/WM/0153, protocol number AC23046, IRAS Project ID 323311) and received NHS management approvals from Lothian, Fife and Forth Valley NHS boards. Our main ethical considerations pertain to the remote administration of the study, such as taking remote consent. To address this, we implemented a consent process, whereby the first step of the consent is done entirely remotely but a member of the research team contacts the participant over the phone to consent participants to the optional, most sensitive, elements of the study. Results will be presented at conferences, published in peer-reviewed journals and communicated to study participants.
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Affiliation(s)
| | - Fasih Haider
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Craig W Ritchie
- Department of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Scottish Brain Sciences, Edinburgh, UK
| | - Graciela Muniz Terrera
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK
- Heritage College of Osteopathic Medicine, Ohio University, Athens, Ohio, USA
| | - Saturnino Luz
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh School of Molecular Genetic and Population Health Sciences, Edinburgh, UK
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Singh S A, Ansari MN, M. Elossaily G, Vellapandian C, Prajapati B. Investigating the Potential Impact of Air Pollution on Alzheimer's Disease and the Utility of Multidimensional Imaging for Early Detection. ACS OMEGA 2024; 9:8615-8631. [PMID: 38434844 PMCID: PMC10905749 DOI: 10.1021/acsomega.3c06328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 12/25/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024]
Abstract
Pollution is ubiquitous, and much of it is anthropogenic in nature, which is a severe risk factor not only for respiratory infections or asthma sufferers but also for Alzheimer's disease, which has received a lot of attention recently. This Review aims to investigate the primary environmental risk factors and their profound impact on Alzheimer's disease. It underscores the pivotal role of multidimensional imaging in early disease identification and prevention. Conducting a comprehensive review, we delved into a plethora of literature sources available through esteemed databases, including Science Direct, Google Scholar, Scopus, Cochrane, and PubMed. Our search strategy incorporated keywords such as "Alzheimer Disease", "Alzheimer's", "Dementia", "Oxidative Stress", and "Phytotherapy" in conjunction with "Criteria Pollutants", "Imaging", "Pathology", and "Particulate Matter". Alzheimer's disease is not only a result of complex biological factors but is exacerbated by the infiltration of airborne particles and gases that surreptitiously breach the nasal defenses to traverse the brain, akin to a Trojan horse. Various imaging modalities and noninvasive techniques have been harnessed to identify disease progression in its incipient stages. However, each imaging approach possesses inherent limitations, prompting exploration of a unified technique under a single umbrella. Multidimensional imaging stands as the linchpin for detecting and forestalling the relentless march of Alzheimer's disease. Given the intricate etiology of the condition, identifying a prospective candidate for Alzheimer's disease may take decades, rendering the development of a multimodal imaging technique an imperative. This research underscores the pressing need to recognize the chronic ramifications of invisible particulate matter and to advance our understanding of the insidious environmental factors that contribute to Alzheimer's disease.
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Affiliation(s)
- Ankul Singh S
- Department
of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology (SRMIST), Kattankulathur, Tamil Nadu 603203, India
| | - Mohd Nazam Ansari
- Department
of Pharmacology and Toxicology, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
| | - Gehan M. Elossaily
- Department
of Basic Medical Sciences, College of Medicine, AlMaarefa University, P.O. Box 71666, Riyadh 13713, Saudi Arabia
| | - Chitra Vellapandian
- Department
of Pharmacology, SRM College of Pharmacy, SRM Institute of Science and Technology (SRMIST), Kattankulathur, Tamil Nadu 603203, India
| | - Bhupendra Prajapati
- Department
of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy,
Shree S.K. Patel College of Pharmaceutical Education and Research, Ganpat University, Gozaria Highway, Mehsana, North Gujarat 384012, India
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Lista S, Santos-Lozano A, Emanuele E, Mercuri NB, Gabelle A, López-Ortiz S, Martín-Hernández J, Maisto N, Imbimbo C, Caraci F, Imbimbo BP, Zetterberg H, Nisticò R. Monitoring synaptic pathology in Alzheimer's disease through fluid and PET imaging biomarkers: a comprehensive review and future perspectives. Mol Psychiatry 2024:10.1038/s41380-023-02376-6. [PMID: 38228892 DOI: 10.1038/s41380-023-02376-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/04/2023] [Accepted: 12/12/2023] [Indexed: 01/18/2024]
Abstract
Alzheimer's disease (AD) is currently constrained by limited clinical treatment options. The initial pathophysiological event, which can be traced back to decades before the clinical symptoms become apparent, involves the excessive accumulation of amyloid-beta (Aβ), a peptide comprised of 40-42 amino acids, in extraneuronal plaques within the brain. Biochemical and histological studies have shown that overaccumulation of Aβ instigates an aberrant escalation in the phosphorylation and secretion of tau, a microtubule-binding axonal protein. The accumulation of hyperphosphorylated tau into intraneuronal neurofibrillary tangles is in turn correlated with microglial dysfunction and reactive astrocytosis, culminating in synaptic dysfunction and neurodegeneration. As neurodegeneration progresses, it gives rise to mild clinical symptoms of AD, which may eventually evolve into overt dementia. Synaptic loss in AD may develop even before tau alteration and in response to possible elevations in soluble oligomeric forms of Aβ associated with early AD. These findings largely rely on post-mortem autopsy examinations, which typically involve a limited number of patients. Over the past decade, a range of fluid biomarkers such as neurogranin, α-synuclein, visinin-like protein 1 (VILIP-1), neuronal pentraxin 2, and β-synuclein, along with positron emission tomography (PET) markers like synaptic vesicle glycoprotein 2A, have been developed. These advancements have facilitated the exploration of how synaptic markers in AD patients correlate with cognitive impairment. However, fluid biomarkers indicating synaptic loss have only been validated in cerebrospinal fluid (CSF), not in plasma, with the exception of VILIP-1. The most promising PET radiotracer, [11C]UCB-J, currently faces significant challenges hindering its widespread clinical use, primarily due to the necessity of a cyclotron. As such, additional research geared toward the exploration of synaptic pathology biomarkers is crucial. This will not only enable their extensive clinical application, but also refine the optimization process of AD pharmacological trials.
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Affiliation(s)
- Simone Lista
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), 47012, Valladolid, Spain.
| | - Alejandro Santos-Lozano
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), 47012, Valladolid, Spain
- Physical Activity and Health Research Group (PaHerg), Research Institute of the Hospital 12 de Octubre ('imas12'), 28041, Madrid, Spain
| | | | - Nicola B Mercuri
- Experimental Neurology Laboratory, IRCCS Santa Lucia Foundation, 00143, Rome, Italy
- Department of Systems Medicine, University of Rome Tor Vergata, 00133, Rome, Italy
| | - Audrey Gabelle
- CMRR, Memory Resources and Research Center, Montpellier University of Excellence i-site, 34295, Montpellier, France
| | - Susana López-Ortiz
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), 47012, Valladolid, Spain
| | - Juan Martín-Hernández
- i+HeALTH Strategic Research Group, Department of Health Sciences, Miguel de Cervantes European University (UEMC), 47012, Valladolid, Spain
| | - Nunzia Maisto
- Laboratory of Pharmacology of Synaptic Plasticity, EBRI Rita Levi-Montalcini Foundation, 00143, Rome, Italy
- Department of Physiology and Pharmacology "V. Erspamer", Sapienza University of Rome, 00185, Rome, Italy
| | - Camillo Imbimbo
- Department of Brain and Behavioral Sciences, University of Pavia, 27100, Pavia, Italy
| | - Filippo Caraci
- Department of Drug and Health Sciences, University of Catania, 95125, Catania, Italy
- Neuropharmacology and Translational Neurosciences Research Unit, Oasi Research Institute-IRCCS, 94018, Troina, Italy
| | - Bruno P Imbimbo
- Department of Research and Development, Chiesi Farmaceutici, 43122, Parma, Italy
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, 431 80, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 80, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, WC1N, London, UK
- UK Dementia Research Institute at UCL, WC1E 6BT, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, 53726, WI, USA
| | - Robert Nisticò
- Laboratory of Pharmacology of Synaptic Plasticity, EBRI Rita Levi-Montalcini Foundation, 00143, Rome, Italy.
- School of Pharmacy, University of Rome "Tor Vergata", 00133, Rome, Italy.
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Narasimhan R, Gopalan M, Sikkandar MY, Alassaf A, AlMohimeed I, Alhussaini K, Aleid A, Sheik SB. Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers. SENSORS (BASEL, SWITZERLAND) 2023; 23:8867. [PMID: 37960568 PMCID: PMC10647614 DOI: 10.3390/s23218867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/22/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023]
Abstract
Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual's activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults' activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.
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Affiliation(s)
- Rajaram Narasimhan
- Centre for Sensors and Process Control, Hindustan Institute of Technology and Science, Chennai 603103, India;
| | - Muthukumaran Gopalan
- Centre for Sensors and Process Control, Hindustan Institute of Technology and Science, Chennai 603103, India;
| | - Mohamed Yacin Sikkandar
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Ahmad Alassaf
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Ibrahim AlMohimeed
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia; (A.A.); (I.A.)
| | - Khalid Alhussaini
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (K.A.); (A.A.)
| | - Adham Aleid
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 12372, Saudi Arabia; (K.A.); (A.A.)
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Chien CF, Sung JL, Wang CP, Yen CW, Yang YH. Analyzing Facial Asymmetry in Alzheimer's Dementia Using Image-Based Technology. Biomedicines 2023; 11:2802. [PMID: 37893175 PMCID: PMC10604711 DOI: 10.3390/biomedicines11102802] [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: 09/01/2023] [Revised: 10/12/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Several studies have demonstrated accelerated brain aging in Alzheimer's dementia (AD). Previous studies have also reported that facial asymmetry increases with age. Because obtaining facial images is much easier than obtaining brain images, the aim of this work was to investigate whether AD exhibits accelerated aging patterns in facial asymmetry. We developed new facial asymmetry measures to compare Alzheimer's patients with healthy controls. A three-dimensional camera was used to capture facial images, and 68 facial landmarks were identified using an open-source machine-learning algorithm called OpenFace. A standard image registration method was used to align the three-dimensional original and mirrored facial images. This study used the registration error, representing landmark superimposition asymmetry distances, to examine 29 pairs of landmarks to characterize facial asymmetry. After comparing the facial images of 150 patients with AD with those of 150 age- and sex-matched non-demented controls, we found that the asymmetry of 20 landmarks was significantly different in AD than in the controls (p < 0.05). The AD-linked asymmetry was concentrated in the face edge, eyebrows, eyes, nostrils, and mouth. Facial asymmetry evaluation may thus serve as a tool for the detection of AD.
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Affiliation(s)
- Ching-Fang Chien
- Department of Neurology, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80756, Taiwan
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80756, Taiwan
| | - Jia-Li Sung
- Department of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Chung-Pang Wang
- Department of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
| | - Chen-Wen Yen
- Department of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
- Department of and Master’s Program in Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Neuroscience Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yuan-Han Yang
- Department of Neurology, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80756, Taiwan
- Department of Neurology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80756, Taiwan
- Department of and Master’s Program in Neurology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Neuroscience Research Center, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
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Amoroso N, Quarto S, La Rocca M, Tangaro S, Monaco A, Bellotti R. An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease. Front Aging Neurosci 2023; 15:1238065. [PMID: 37719873 PMCID: PMC10501457 DOI: 10.3389/fnagi.2023.1238065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 08/08/2023] [Indexed: 09/19/2023] Open
Abstract
The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Silvano Quarto
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Marianna La Rocca
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica, Universitá degli Studi di Bari Aldo Moro, Bari, Italy
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