The depressed brain in Parkinson's disease: Implications for an inflammatory biomarker

Global biomarker trends in Parkinson's disease research: A bibliometric analysis

As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.

Global biomarker trends in Parkinson's disease research: A bibliometric analysis

As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.

Avoiding ecosystem and social impacts of hydropower, wind, and solar in Southern Africa's low-carbon electricity system

The scale at which low-carbon electricity will need to be deployed to meet economic growth, electrification, and climate goals in Africa is unprecedented, yet the potential land use and freshwater impacts from this massive build-out of energy infrastructure is poorly understood. In this study, we characterize low-impact onshore wind, solar photovoltaics, and hydropower potential in Southern Africa and identify the cost-optimal mix of electricity generation technologies under different sets of socio-environmental land use and freshwater constraints and carbon targets. We find substantial wind and solar potential after applying land use protections, but about 40% of planned or proposed hydropower projects face socio-environmental conflicts. Applying land and freshwater protections results in more wind, solar, and battery capacity and less hydropower capacity compared to scenarios without protections. While a carbon target favors hydropower, the amount of cost-competitively selected hydropower is at most 45% of planned or proposed hydropower capacity in any scenario-and is only 25% under socio-environmental protections. Achieving both carbon targets and socio-environmental protections results in system cost increases of 3-6%. In the absence of land and freshwater protections, environmental and social impacts from new hydropower development could be significant.

Global transcontinental power pools for low-carbon electricity

The transition to low-carbon electricity is crucial for meeting global climate goals. However, given the uneven spatial distribution and temporal variability of renewable resources, balancing the supply and demand of electricity will be challenging when relying on close to 100% shares of renewable energy. Here, we use an electricity planning model with hourly supply-demand projections and high-resolution renewable resource maps, to examine whether transcontinental power pools reliably meet the growing global demand for renewable electricity and reduce the system cost. If all suitable sites for renewable energy are available for development, transcontinental trade in electricity reduces the annual system cost of electricity in 2050 by 5-52% across six transcontinental power pools compared to no electricity trade. Under land constraints, if only the global top 10% of suitable renewable energy sites are available, then without international trade, renewables are unable to meet 12% of global demand in 2050. Introducing transcontinental power pools with the same land constraints, however, enables renewables to meet 100% of future electricity demand, while also reducing costs by up to 23% across power pools. Our results highlight the benefits of expanding regional transmission networks in highly decarbonized but land-constrained future electricity systems.

A Predictive Visual Analytics System for Studying Neurodegenerative Disease Based on DTI Fiber Tracts

Diffusion tensor imaging (DTI) has been used to study the effects of neurodegenerative diseases on neural pathways, which may lead to more reliable and early diagnosis of these diseases as well as a better understanding of how they affect the brain. We introduce a predictive visual analytics system for studying patient groups based on their labeled DTI fiber tract data and corresponding statistics. The system's machine-learning-augmented interface guides the user through an organized and holistic analysis space, including the statistical feature space, the physical space, and the space of patients over different groups. We use a custom machine learning pipeline to help narrow down this large analysis space and then explore it pragmatically through a range of linked visualizations. We conduct several case studies using DTI and T1-weighted images from the research database of Parkinson's Progression Markers Initiative.

An all-Africa dataset of energy model "supply regions" for solar photovoltaic and wind power

With solar and wind power generation reaching unprecedented growth rates globally, much research effort has recently gone into a comprehensive mapping of the worldwide potential of these variable renewable electricity (VRE) sources. From a perspective of energy systems analysis, the locations with the strongest resources may not necessarily be the best candidates for investment in new power plants, since the distance from existing grid and road infrastructures and the temporal variability of power generation also matter. To inform energy planning and policymaking, cost-optimisation models for energy systems must be fed with adequate data on potential sites for VRE plants, including costs reflective of resource strength, grid expansion needs and full hourly generation profiles. Such data, tailored to energy system models, has been lacking up to now. In this study, we present a new open-source and open-access all-Africa dataset of "supply regions" for solar photovoltaic and onshore wind power to feed energy models and inform capacity expansion planning.

Evaluation of α-synuclein and apolipoprotein E as potential biomarkers in cerebrospinal fluid to monitor pharmacotherapeutic efficacy in dopamine dictated disease states of Parkinson's disease and schizophrenia

BACKGROUND AND OBJECTIVE

Dopamine plays an important role in the disease pathology of Parkinson's disease and schizophrenia. These two neuropsychiatric disorders represent disease end points of the dopaminergic spectrum where Parkinson's disease represents dopamine deficit and schizophrenia represents dopamine hyperactivity in the mid-brain. Therefore, current treatment strategies aim to restore normal dopamine levels. However, during treatment patients develop adverse effects due to overshooting of physiological levels of dopamine leading to psychosis in Parkinson's disease, and extrapyramidal symptoms in schizophrenia. Absence of any laboratory tests hampers modulation of pharmacotherapy. Apolipoprotein E and α-synuclein have an important role in the neuropathology of these two diseases. The objective of this study was to evaluate cerebrospinal fluid (CSF) concentrations of apolipoprotein E and α-synuclein in patients with these two diseases so that they may serve as biomarkers to monitor therapy in Parkinson's disease and schizophrenia.

METHODS

Drug-naïve Parkinson's disease patients and Parkinson's disease patients treated with dopaminergic therapy, neurological controls, schizophrenic patients treated with antidopaminergic therapy, and drug-naïve schizophrenic patients were recruited for the study and CSF was collected. Enzyme-linked immunosorbent assays were carried out to estimate the concentrations of apolipoprotein E and α-synuclein. Pathway analysis was done to establish a possible role of these two proteins in various pathways in these two dopamine dictated diseases.

RESULTS

Apolipoprotein E and α-synuclein CSF concentrations have an inverse correlation along the entire dopaminergic clinical spectrum. Pathway analysis convincingly establishes a plausible hypothesis for their co-regulation in the pathogenesis of Parkinson's disease and schizophrenia. Each protein by itself or as a combination has encouraging sensitivity and specificity values of more than 55%.

CONCLUSION

The dynamic variation of these two proteins along the spectrum is ideal for them to be pursued as pharmacotherapeutic biomarkers in CSF to monitor pharmacological efficacy in Parkinson's disease and schizophrenia.