Parkinson’s disease (PD) is a neurodegenerative disorder characterized by alpha-synuclein accumulation and loss of dopaminergic neurons in the substantia nigra (SN) region of the brain. Increased levels of alpha-synuclein have been shown to result in loss of mitochondrial electron transport chain complex I activity leading to increased reactive oxygen species (ROS) production. WT alpha-synuclein was stably overexpressed in human BE(2)-M17 neuroblastoma cells resulting in increased levels of an alpha-synuclein multimer, but no increase in alpha-synuclein monomer levels. Oxygen consumption was decreased by alpha-synuclein overexpression, but ATP levels did not decrease and ROS levels did not increase. Treatment with ferrous sulfate, a ROS generator, resulted in decreased oxygen consumption in both control and alpha-synuclein overexpressing cells. However, this treatment only decreased ATP levels and increased ROS production in the cells overexpressing alpha-synuclein. Similarly, paraquat, another ROS generator, decreased ATP levels in the alpha-synuclein overexpressing cells, but not in the control cells, further demonstrating how alpha-synuclein sensitized the cells to oxidative insult. Proteomic analysis yielded molecular insights into the cellular adaptations to alpha-synuclein overexpression, such as the increased abundance of many mitochondrial proteins. Many amino acids and citric acid cycle intermediates and their ester forms were individually supplemented to the cells with l-serine, l-proline, l-aspartate, or l-glutamine decreasing ROS production in oxidatively stressed alpha-synuclein overexpressing cells, while diethyl oxaloacetate or l-valine supplementation increased ATP levels. These results suggest that dietary supplementation with individual metabolites could yield bioenergetic improvements in PD patients to delay loss of dopaminergic neurons.

Abstract Parkinson’s disease and Huntington’s disease are progressive neurodegenerative movement disorders that typically manifest in adulthood. In this study, genetic risk factors contributing to these two movement disorders were investigated in Finnish patients. Patients with early-onset or late-onset Parkinson’s disease as well as population controls were examined. The p.L444P mutation in GBA was found to contribute to the risk of Parkinson’s disease. POLG1 compound heterozygous mutations were detected in two patients with Parkinson’s disease and rare length variants in POLG1 were associated with Parkinson’s disease. Variants in SMPD1, LRRK2 or CHCHD10, previously detected in other populations, were not detected, suggesting that they are rare or even absent in the Finnish population. Patients with Huntington’s disease were investigated for HTT gene haplotypes as well as whether these haplotypes alter the stability of the elongated CAG repeat. Haplogroup A was less common in Finns than in other European populations, whereas it was significantly more common in patients with Huntington’s disease than in the general population. Certain HTT haplotypes as well as the parental gender were found to affect the repeat instability. We found that compound heterozygous mutations in POLG1 were causative of Parkinson’s disease, rare length variants in POLG1 were associated with Parkinson’s disease and GBA p.L444P was significantly more frequent in patients than in the controls, which suggests that these mutations are associated with the development of Parkinson’s disease. The low prevalence of Huntington’s disease in Finland correlates with the low frequency of the disease-associated HTT haplogroup A. Paternal inheritance combined with haplotype A1 increased the risk of repeat expansion. Movement disorders in Finland were found to share some of the same genetic risk factors found in other European populations, but some other recognized genetic variants could not be detected. / Tiivistelmä Parkinsonin tauti ja Huntingtonin tauti ovat hermostoa rappeuttavia eteneviä liikehäiriösairauksia, jotka tyypillisesti ilmenevät aikuisiällä. Tässä tutkimuksessa selvitettiin näiden kahden liikehäiriösairauden geneettisiä riskitekijöitä suomalaisilla potilailla. Tutkimme potilaita, joilla oli varhain alkava Parkinsonin tauti tai myöhään alkava Parkinsonin tauti sekä väestökontrolleja. GBA-geenin p.L444P mutaation havaittiin lisäävän Parkinsonin taudin riskiä. Kaksi Parkinsonin tautia sairastavaa potilasta oli yhdistelmäheterotsygootteja haitallisten POLG1-geenin varianttien suhteen ja harvinaiset POLG1 CAG toistojaksovariantit assosioituivat Parkinsonin tautiin. Tutkittuja variantteja SMPD1-, LRRK2- ja CHCHD10-geeneissä ei löydetty tästä aineistosta lainkaan, mikä viittaa siihen, että ne puuttuvat suomalaisesta väestöstä tai ovat harvinaisia. Huntingtonin tautia sairastavilta potilailta tutkittiin HTT-geenin haploryhmiä ja niiden vaikutusta Huntingtonin tautia aiheuttavan pidentyneen toistojakson epästabiiliuteen. Haploryhmä A oli suomalaisessa väestössä harvinainen verrattuna eurooppalaiseen väestöön ja se oli huomattavasti yleisempi Huntingtonin tautipotilailla kuin väestössä. Toistojakson epästabiiliuteen vaikuttivat tietyt HTT-geenin haplotyypit samoin kuin sen vanhemman sukupuoli, jolta pidentynyt toistojakso periytyy. POLG1 yhdistelmäheterotsygoottien katsottiin aiheuttavat Parkinsonin tautia ja harvinaisten POLG1 CAG toistojaksovarianttien todettiin assosioituvan Parkinsonin tautiin Suomessa. GBA p.L444P mutaatio merkittävästi yleisempi Parkinsonin tautipotilailla kuin kontrolleilla, mikä viittaa siihen, että se on Parkinsonin taudin riskitekijä. Huntingtonin tautiin assosioituvan haploryhmä A:n matala frekvenssi selittää taudin vähäistä esiintyvyyttä Suomessa. Paternaalinen periytyminen ja haplotyyppi A1 lisäsivät HTT-geenin toistojakson pidentymisen riskiä. Liikehäiriösairauksilla todettiin Suomessa osittain samanlaisia riskitekijöitä kuin muualla Euroopassa, mutta kaikkia tutkittuja variantteja emme havainneet.

En la presente Tesis Doctoral se describen los cambios celulares que ocurren en la retina en la enfermedad de Parkinson y su posible uso como biomarcadores tempranos de la enfermedad. Los pacientes con enfermedad de Parkinson poseen acumulaciones de alfa sinucleína fosforilada en la retina similares a las que se encuentran en el cerebro de los mismos pacientes. De hecho, la cantidad de alfa-sinucleína fosforilada en la retina correlaciona con la cantidad de alfa-sinucleína fosforilada en el cerebro, con el estadio de progresión de la enfermedad y con la severidad de los síntomas motores. Además, en la retina de enfermos de párkinson se describe una degeneración de las células ganglionares melanopsínicas de la retina, lo que podría explicar las alteraciones en los ritmos circadianos y los desórdenes del sueño que aparecen en pacientes. Finalmente, también se muestra la degeneración de las células amacrinas dopaminérgicas, que se reducen en un 45%. Este fallo en el sistema dopaminérgico de la retina provoca alteraciones morfológicas en las células amacrinas AII, sus principales postsinápticas, y podría explicar algunas alteraciones visuales descritas en la enfermedad como la disminución de la sensibilidad al contraste o de la agudeza visual. En global, los resultados muestran que la retina reproduce los procesos degenerativos que ocurren en el cerebro en la enfermedad de Parkinson y, por tanto, que es un tejido idóneo para el estudio de la enfermedad. Además, el estudio de la retina aporta información sobre el estadio de la enfermedad y puede ser empleado como un biomarcador temprano que ayude al diagnóstico y seguimiento de la misma.

Gångproblematiken hos personer med Parkinsons sjukdom är svårhanterad av både vårdtagare och vårdare och kräver mycket kunskap och förståelse. Syftet med denna litteraturstudie var att identifiera omvårdnadsåtgärder som kan underlätta gångproblematik hos personer med Parkinsons sjukdom. Tio kvantitativa studier har används i denna litteraturstudie och sökningar har gjorts i Pubmed och i CINAHL. Resultatet från dessa sökningar presenterades i fyra strategier och fynden var rehabilitering, individanpassad träning, stimuli och användandet av dagböcker. Dessa strategier kan användas som verktyg för en sjuksköterska när hon interagerar med personer med Parkinsons sjukdom. Genom en ökad insikt i hur det går att underlätta den specifika gångproblematiken kan omvårdnaden optimeras. / Gait disturbance in persons with Parkinson’s disease is difficult to manage by both caretakers and caregivers and it requires much knowledge and understanding. The aim of this literature review was to identify means to aid the gait disturbance in persons with Parkinson’s disease. Ten quantitative studies have been used in this literature review and searches were carried out in Pubmed and CINAHL. The results from these searches were presented in four strategies and the findings were rehabilitation, individual exercise schedules, cueing and the use of diaries. These strategies can be used by a nurse as tools while she is interacting with persons with Parkinson’s disease. With a greater insight on the specific gait disturbances nursing can be optimized.

Parkinson’s disease (PD) is frequently compounded by neruropsychiatric complications, increasing disability. The combined effect of motor and mental status on care-dependency in PD outpatients is not well characterized. We conducted a cross-sectional study of 1449 PD outpatients. The assessment comprised the Montgomery–Asberg Depression Rating Scale (MADRS) and the diagnostic criteria for dementia. PD severity and treatment complications were rated using Hoehn and Yahr staging and the Unified Parkinson’s Disease Rating Scale (UPDRS) IV. The acknowledged level of care-dependency was documented. Care-dependency was present in 18.3% of all patients. A total of 13.9% had dementia, 18.8% had depression, and 14.3% had both. Regression analyses revealed increasing effects of age, PD duration, and PD severity on care-dependency in all three mental-disorder subgroups with the strongest effects in patients with depression only. Depressed patients with antidepressive treatment still had significantly higher PD severity, higher MADRS and UPDRS-IV scores but were not more likely to be care-dependent than non-depressed patients. Older age, longer duration and increased severity of PD contribute to care-dependency in patients with untreated depression. Treatment of depression is associated with lower rates of care-dependency.

The neurotoxin 6-hydroxydopamine (6-OHDA), following pretreatment with the norepinephrine transport inhibitor desipramine, selectively destroys dopaminergic neurons. When given to rats, neonatal 6-OHDA (n6-OHDA) crosses the blood-brain barrier to destroy 90-99% of dopaminergic nerves in pars compacta substantia nigra (SNpc). The n6-OHDA-lesioned rat is posed as a reasonable animal model for PD: (a) the magnitude of dopaminergic neuronal destruction is expansive, (b) mapping of dopaminergic denervation has been defined, (c) effects on dopamine (DA) receptor alterations have been elucidated (d) as well as changes in receptor sensitivity status, (e) reactive sprouting of serotoninergic innervation (i.e. hyperinnervation) has been mapped, and (f) interplay between serotoninergic and dopaminergic systems is characterized. (g) A broad range of locomotor and stereotyped behaviors has been assessed and (h) large numbers of neurochemical assessments have been attained. (i) n6-OHDA-lesioned rats survive 6-OHDA lesioning and (j) the rat is behaviorally indistinguishable from controls. Dopaminergic destruction in early ontogeny rather in adulthood is a 'treatment liability' of this model, yet other animal models have liability issues of a serious nature-the initial one being use of a neurotoxin to produce the animal model of PD. The n6-OHDA-lesioned rat is proposed as a PD model for its value in associating the SNpc dopaminergic lesion with behavioral outcomes, also for replicability of dopaminergic destruction, and the accompanying neuronal adaptations and interplay between neuronal phenotypes in brain-which provide a means to better define and understand the range of deficits and neuronal adaptations that are likely to occur in human PD.

Yes / This study introduces a quantum-inspired computational paradigm to address the unresolved problem of Convolutional Neural Networks (CNNs) using the Rectified Linear Unit (ReLU) activation function (AF), i.e., the ‘dying ReLU’. This problem impacts the accuracy and the reliability in image classification tasks for critical applications, such as in healthcare. The proposed approach builds on the classical ReLU and Leaky ReLU, applying the quantum principles of entanglement and superposition at a computational level to derive two novel AFs, respectively the ‘Quantum ReLU’ (QReLU) and the ‘modified-QReLU’ (m-QReLU). The proposed AFs were validated when coupled with a CNN using seven image datasets on classification tasks involving the detection of COVID-19 and Parkinson’s Disease (PD). The out-of-sample/test classification accuracy and reliability (precision, recall and F1-score) of the CNN were compared against those of the same classifier when using nine classical AFs, including ReLU-based variations. Findings indicate higher accuracy and reliability for the CNN when using either QReLU or m-QReLU on five of the seven datasets evaluated. Whilst retaining the best classification accuracy and reliability for handwritten digits recognition on the MNIST dataset (ACC = 99%, F1-score = 99%), avoiding the ‘dying ReLU’ problem via the proposed quantum AFs improved recognition of PD-related patterns from spiral drawings with the QReLU especially, which achieved the highest classification accuracy and reliability (ACC = 92%, F1-score = 93%). Therefore, with these increased accuracy and reliability, QReLU and m-QReLU can aid critical image classification tasks, such as diagnoses of COVID-19 and PD. / The authors declare that this was the result of a HEIF 2020 University of Bradford COVID-19 response-funded project ‘Quantum ReLU-based COVID-19 Detector: A Quantum Activation Function for Deep Learning to Improve Diagnostics and Prognostics of COVID-19 from Non-ionising Medical Imaging’. However, the funding source was not involved in conducting the study and/or preparing the article.

Background Individuals with Parkinson’s disease (PD), Alzheimer’s disease (AD), multiple sclerosis (MS), and stroke experience various cognitive and motor impairments, which can negatively affect their ability to complete daily activities such as walking and talking. Walking and talking or dual-task walking often leads to a decline in performance in one or both tasks, which is called dual-task cost. This dual-task cost seems to be more pronounced in individuals with neurological conditions compared to age-matched healthy individuals, possibly due to disease-associated impairments. While the results of neuroimaging studies are inconsistent, several studies have found structural or functional brain changes that might contribute to a decrease in dual-task walking performance in people with neurological disorders. Research question/objective The objective of this study was to systematically review peer-reviewed articles that examined the neural correlates of cognitive-motor dual-task interference in people with neurological conditions. The primary aim was to identify brain areas or measures that might underlie dual-task walking performance of people with MS, stroke, AD, and PD. The secondary aim was to compare their dual-task performance with other groups such as healthy individuals. Methods A systematic review of the literature was conducted, following PRISMA guidelines, on Medline, Embase, and Scopus databases. Studies were included if they examined dual-task walking performance and associated structural or functional brain changes in adults with stroke, MS, PD, and AD. Studies were first screened using a title and abstract and then full-text review was performed. The quality of each study was assessed using the Joanna Briggs Institute (JBI) critical appraisal checklist and then the data regarding cognitive and motor performance during dual- versus single task conditions and brain imaging were extracted. The findings were grouped according to neurological condition and then by imaging technique. Results After screening, 23 studies were selected to be included in this review. The majority (90%) showed a decline in dual-task walking performance compared to single-walking in people with neurological conditions and this decline was greater than healthy individuals. Most structural imaging studies (75%) reported a significant positive correlation between lower brain structural integrity and poorer dual-task walking performance. Specifically, the striatum regions including pedunculopontine nucleus and hippocampus in PD demonstrated this positive correlation. In MS, the supplementary motor area showed a positive correlation. In terms of functional brain changes, 60% observed an increase in prefrontal cortex activity during dual tasking in people with PD and stroke, which was associated with decreased performance in most cases (n = 3) while some found an association with maintained performance (n = 2). Further, people with MS and stroke both showed a significant relationship between a higher supplementary motor area activity and poor dual-task walking performance. Conclusions This systematic review identified several structural and functional neural correlates of dual-task walking in people with PD, MS, and stroke and has facilitated a better understanding of neural basis of dual-task interference in people with neurological conditions. However, the relationship between the brain and behavioural outcomes is complicated and various factors may influence neural correlates, such as individuals’ characteristics (e.g., neural reserve, age), the nature of cognitive task used, and presentation modality (e.g., visual).

Parkinson’s disease is one of the most common neurodegenerative disorders worldwide, characterized by a progressive loss of dopaminergic neurons mainly localized in the substantia nigra pars compacta. In recent years, the detailed analyses of both genetic and idiopathic forms of the disease have led to a better understanding of the molecular and cellular pathways involved in PD, pointing to the centrality of mitochondrial dysfunctions in the pathogenic process. Failure of mitochondrial quality control is now considered a hallmark of the disease. The peroxisome proliferator-activated receptor gamma coactivator 1 (PGC-1) family acts as a master regulator of mitochondrial biogenesis. Therefore, keeping PGC-1 level in a proper range is fundamental to guarantee functional neurons. Here we review the major findings that tightly bond PD and PGC-1s, raising important points that might lead to future investigations.

This critical synopsis of prior work by Luca Parisi is submitted in support of a PhD by Published Work. The work focuses on deriving accurate, reliable and explainable clinical decision support systems as minimum clinically viable solutions leveraging Machine Learning (ML) and evolutionary algorithms, for the first time, to facilitate early diagnostic predictions of Parkinson's Disease and hypothermia in hospitals, as well as prognostic predictions of optimal postoperative recovery area and of chronic hepatitis. Despite the various pathological aetiologies, the underlying capability of ML-based algorithms to serve as a minimum clinically viable solution for predicting early diagnosis and prognosis has been thoroughly demonstrated. Feature selection (FS) is a proven method for increasing the performance of ML-based classifiers for several applications. Although advances in ML, such as Deep Learning (DL), have denied the usefulness of any extrinsic FS by incorporating it in their architectures, e.g., convolutional filters in convolutional neural networks, DL algorithms often lack the required explainability to be understood and interpreted by clinicians within the context of the diagnostic and prognostic tasks of interest. Their relatively complicated architectures, the hardware required for running them and the limited explainability or interpretability of their architectures, the decision-making process – although as assistive tools - driven by the algorithms’ training and predictive outcomes have hindered their application in a clinical setting. Luca Parisi’s work fills this translational research gap by harnessing the explainability of using traditional ML- and evolutionary algorithms-based FS methods for improving the performance of ML-based algorithms and devise minimum viable solutions for diagnostic and prognostic purposes. The work submitted here involves independent research work, including collaborative studies with Marianne Lyne Manaog (MedIntellego®) and Narrendar RaviChandran (University of Auckland). In particular, conciliating his work as a Senior Artificial Intelligence Engineer and volunteering commitment as the President and Research Committee Leader of a student-led association named the “University of Auckland Rehabilitative Technologies Association”, Luca Parisi decided to embark on most research works included in this synopsis to add value to society via accurate, reliable and explainable, hence clinically viable applications of AI. The key findings of these studies are: (i) ML-based FS algorithms are sufficient for devising accurate, reliable and explainable ML-based classifiers for aiding prediction of early diagnosis for Parkinson’s Disease and chronic hepatitis; (ii) evolutionary algorithms-based optimisation is a preferred method for improving the accuracy and reliability of decision support systems aimed at aiding early diagnosis of hypothermia; (iii) evolutionary algorithms-based optimisation methods enable to devise optimised ML-based classifiers for improving postoperative discharge; (iv) whilst ML-based algorithms coupled with ML based FS methods are the minimum clinically viable solution for binary classification problems, ML-based classifiers leveraging evolutionary algorithms for FS yield more accurate and reliable predictions, as reducing the search space and overlapping regions for tackling multi-class classification problems more effectively, which involve a higher number of degrees of freedom. Collectively, these findings suggest that, despite advances in ML, state-of-the-art ML algorithms, coupled with ML-based or evolutionary algorithms for FS, are enough to devise accurate, reliable and explainable decision support systems for performing both an early diagnosis and a prediction of prognosis of various pathologies.