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Compassionate mind training for people with Parkinson's disease: A pilot study and predictors of response
Get PDFData Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.Supporting Information is available online at: https://onlinelibrary.wiley.com/doi/10.1111/ene.16286#support-information-section .Elena Makovac and Lucia Ricciardi share senior authorship.Introduction:
People with Parkinson's disease (PD) often present with disabling neuropsychiatric symptoms. Compassionate mind training (CMT) is a psychological approach effective in reducing stress and promoting psychological well-being. Heart rate variability (HRV), a measure reflecting sympathovagal balance, has been associated with psychological well-being and a compassionate attitude.
Aim:
To assess the feasibility and effectiveness of CMT in enhancing the quality of life and psychological well-being in PD patients. Additionally, we evaluated HRV as a physiomarker for assessing the CMT outcomes.
Methods:
Twenty-four PD patients participated in the study. A 6-week online CMT intervention was delivered on a weekly basis. At baseline and post-intervention patients completed questionnaires assessing depression, anxiety and quality of life. In a subsample of 11 patients, HRV was measured at baseline and post-intervention in three conditions: at rest, during stress and after 3 min of deep breathing.
Results:
The attendance rate was 94.3%. Quality of life and perceived stigma improved post-intervention as compared with baseline (p = 0.02 and p = 0.03 for PD Questionnaire-39 total score and Stigma subscore, respectively). After CMT, patients presented better physiological regulation to stress, as measured by higher HRV as compared with baseline (p = 0.005). Notably, patients who were more resilient to stress at baseline (less decrease in HRV during stress) experienced a more substantial reduction in anxiety and depression following CMT.
Conclusions:
CMT is feasible and can improve quality of life and stigma in PD patients. HRV emerges as a promising physiomarker for predicting and measuring the outcomes of psychological interventions in PD.No funding was received for this research
“It Is as if I Gave a Gift to Myself”: A Qualitative Phenomenological Study on Working Adults’ Leisure Meaning, Experiences, and Participation
Get PDFData Availability Statement: All necessary data are available from the manuscript. The authors will share the available dataset if required.Leisure participation is a fundamental human and occupational right throughout life for working people, particularly in adulthood. A total of 28 working adults representing diverse regions of Turkey, from middle-class backgrounds, aged between 25 and 50, and without any known health conditions, were interviewed to gain insights into their leisure participation during the period September 2021–May 2022. The acquired data were analysed using the interpretative phenomenological analysis (IPA) approach. The analysis identified six main themes and twenty-two subthemes: the meaning of leisure, recovery from work, facilitators and barriers, well-being, occupational injustice, and flow of life. Participants distinguished between “free time” and “leisure time”, defining the latter as purposeful engagement in enjoyable, meaningful activities. This study emphasises the dynamic interplay of factors influencing leisure participation among Turkish working adults, including working conditions, financial resources, social support systems, and opportunities for participation, with some effects of COVID-19 pandemic. One can shift from well-being to a lack of well-being, and this can result in occupational injustices that may arise in the flow of life, as unsupportive consequences of participation limitations among working adults. By acknowledging and enhancing leisure as a crucial aspect of well-being, this research underscores the importance of promoting resilience and holistic health among working individuals.This research received no external funding
Graph neural network-based subgraph analysis for predicting adverse drug events
Get PDFData availability: This study obtained research data from an Australian private health insurance organization (Commonwealth Bank Health Society, CBHS). This data was collected in a de-identified format and through a research agreement between the CBHS and the University of Sydney (University of Sydney reference number: CT18435). For reproducing the results of this study, the relevant data and codes of the study can be accessed from this repository: https://doi.org/10.5281/zenodo.7703238.Purpose:
Adverse drug events (ADEs) are a significant global public health concern, and they have resulted in high rates of hospital admissions, morbidity, and mortality. Prior to the use of machine learning and deep learning methods, ADEs may not become well recognized until long after a drug has been approved and is widely used, which poses a significant challenge for ensuring patient safety. Consequently, there is a need to develop computational approaches for earlier identification of ADEs not detected during pre-registration clinical trials.
Methods:
This paper presents a state-of-the-art network-based approach that models patients as subgraphs composed of nodes of International Classification of Diseases (ICD) codes and directed edges illustrating disease progression. Four Graph Neural Network (GNN) variants were employed to make sub-graph level predictions that answer three Research Questions (RQ): 1) whether ADE(s) would occur given a patient's prior diagnoses history, 2) when an ADE would occur, and 3) which ADE would occur. The first and second RQs were addressed using a binary classification approach. The third RQ was addressed using a multi-label classification model.
Results:
The proposed network-based approach demonstrated superior performance in predicting ADEs, with the GraphSage model exhibiting the highest accuracy for both RQ 1 (0.8863) and RQ 3 (0.9367), while the Graph Attention Networks (GAT) model was found to perform best for RQ 2 (0.8769). Furthermore, an analysis segmented by ADE classification revealed that while RQs 1 and 3 exhibited minimal variance across different ADE categories, a distinct advantage was observed for categories B, C, and E in the context of RQ 2 when applying this sub-graph method.
Conclusion:
The network-based approach demonstrates the potential of GNNs in supporting the early detection and prevention of ADEs. Accurately predicting ADEs could enable healthcare professionals to make informed clinical decisions, take preventive measures and adjust medication regimens before serious adverse events occur. The proposed prediction method could also lead to optimized usage of healthcare resources by preventing hospital admissions and reducing the overall burden of adverse drug events on the healthcare systems.MK is supported by UKRI NERC grant NE /X000192/12
An Analytical Study for Explosive Grain Initiation
Get PDFData Availability Statement: A MATLAB code to calculate explosive lines for different compositions is attached in the manuscript as a Supplementary File available online at: https://www.mdpi.com/2073-4352/14/11/940#app1-crystals-14-00940 . Any other data presented in this study are available on request from the corresponding author.The most common form of solidification of metals is heterogeneous nucleation, in which the particles, regardless of whether they are endogenous or exogenous, nucleate the primary crystal phase, becoming solid crystal particles and, subsequently, initiating into grains during solidification. Explosive grain initiation has been proposed recently for these particles, which have significant nucleation undercooling, in which once nucleation happens, a certain number of solid particles can initiate into grains simultaneously, resulting in recalescence. This is a different form of grain initiation and has high potential for more significant grain refinement in casting alloys. In this work, an analytical model is designed to describe explosive grain initiation, based on which the criteria for the three different grain initiation forms, explosive grain initiation (EGI), hybrid grain initiation (HGI), and progressive grain initiation (PGI), are derived. These criteria are employed to develop a grain initiation map for the Mg-Al alloy system inoculated with nucleant particles having a log-normal size distribution. This work can not only help us to understand the effect of each condition, such as the cooling rate and the solute concentration, on grain initiation behaviors, but also predict the grain size for alloy systems with relatively impotent nucleant particles during solidification.This research was financially supported by EPSRC (UK) under UKRI Interdisciplinary Centre for CircularMetal, with grant number EP/V011804/1
Tone and Capital Investments
Get PDFData Availability: All data used in this paper have been obtained from publicly available sources identified in the paper. Data used in this study will be provided upon reasonable request.This study contributes to the literature on the relationship between the tone of financial disclosure narratives and capital investments. Specifically, we examine conditions where managers have differential incentives to disclose incrementally informative and misleading investment narratives. First, we argue that managers have fewer incentives to disclose misleading investment narratives if their content can be verified from concurrently disclosed numbers. Consistent with this argument, we find that the tone of a sample of 10-K disclosures is positively associated with current-period investments, suggesting that managers disclose incrementally informative narratives on the investment level. Second, we argue that, when the investment outcomes hamper their interests, managers have heightened incentives to disclose misleading investment efficiency narratives, as investment efficiency is not readily verifiable from concurrently disclosed numbers. Consistent with this argument, we find that the tone is more negatively associated with investment efficiency when firms: (a) undertake large vis a vis small investments (b) undertake vis a vis do not undertake new investments in the year (c) overinvest vis a vis underinvest and (d) decrease vis a vis increase investment efficiency. Overall, our results suggest that managers may disclose misleading narratives when the investment outcomes misalign with their interests.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors
The cognitive sequalae of COVID-19 and long COVID - the inconspicuous wound of the pandemic
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonA novel disease named coronavirus disease 2019 (COVID-19) was discovered in the year 2019, and soon after declared a pandemic by the World Health Organization. Coronaviruses are neurotropic in nature and since the emergence of COVID-19 many studies have reported neuropsychological symptoms in infected individuals, including headaches, dizziness, seizures, depression, and also cognitive deficits. These symptoms can persist for many weeks to months and are commonly referred to as long COVID. This thesis, therefore, aimed to examine the neuropsychological impact of COVID-19, more specifically on cognitive function and psychological well-being cross-sectionally in a working-age sample, and in a sub-sample longitudinally. Furthermore, it explored the impact of long COVID on brain structures, again in a working-age sample.
Three empirical studies were conducted, study one was a behavioural study investigating the effects of COVID-19 on cognitive function (processing speed, attention, working memory, executive function, and memory), and the associations of physical and mental health (specifically, depression, anxiety, stress, and sleep) with cognitive function in adults (N = 222) from the general population. Study two involved a follow-up of the sample investigated in study one to determine the longitudinal impact of COVID-19 and long-COVID symptoms on cognitive function, mental health, and sleep. Study three used whole brain magnetic resonance imaging to examine the association of persistent COVID-19 symptoms with grey matter, white matter, cerebral spinal fluid and various subcortical brain volumes (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus), and its association with cognitive function, mental health, and sleep in a working-age, general population sample (N = 43) of COVID-19 survivors.
The findings of study one showed significantly larger processing speed intra-individual variability, on average, in the COVID group, relative to the non-COVID group, with no significant difference observed in other cognitive variables. However, participants who required hospitalisation due to their diagnosis of COVID-19, relative to those who did not, showed poorer cognitive function in multiple domains, and total long-COVID symptom load was negatively associated with performance in all cognitive domains. In study two, a trend-level improvement at the six-month follow-up was observed in processing speed intra-individual variability in the COVID group with no significant change in the non-COVID group. A significant reduction in total long-COVID symptom load occurred at follow-up, and this correlated with an improvement in executive function, especially in the non-hospitalised COVID group. However, cognitive disruption persisted in COVID group participants with a history of hospitalisation and/or long-COVID symptoms. In study three, total persistent COVID-19 symptom load was significantly associated with smaller putamen volume, multiple cognitive domains, mental health, and sleep quality (medium-to-large effect sizes). Smaller putamen volume was also correlated with a disruption in multiple cognitive domains and poorer sleep quality, though only the relationship between lower executive function and persistent COVID-19 symptom load was mediated by smaller putamen volume.
In conclusion, the findings showed a negative impact of COVID-19 on cognitive function, although to a lesser extent in this working-age sample than those reported earlier in older samples, and some improvement was visible after a six-month period. Hospitalisation history and long-COVID symptoms, however, were associated with wide-spread disruption in cognitive function and poor sleep quality. The disruption in cognitive function, in particular executive function, due to persistent COVID-19 symptoms seemed to be mediated by smaller putamen volume. These findings provide further insight into the relationship between COVID-19 and cognitive function, its effect on the brain, and the potential serious impact of hospitalisation history and long COVID on cognitive function and brain health. Overall, our findings suggest a need for longitudinal monitoring as well as remediation and support for those individuals who were hospitalised when acutely ill and/or have a diagnosis of long COVID
The role of dendritic cells in tertiary lymphoid structures: implications in cancer and autoimmune diseases
Get PDFIn the published article, there was an error in the Funding statement. The section originally stated that “COST is supported by the EU Framework Program Horizon 2020”, while it should refer to “Horizon Europe”. The correct Funding statement appears below.
“The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was developed within the scope of projects with references UIDB/04501/2020 and https://doi.org/10.54499/UIDB/04501/2020, UIDP/04501/2020 and https://doi.org/10.54499/UIDP/04501/2020, 2022.03217.PTDC and DOI 10.54499/2022.03217.PTDC, financially supported by national funds (OE), through FCT - Fundação para a Ciência e Tecnologia, I.P./MCTES. This work was also supported by the World Scleroderma Foundation and Edit Busch Stiftung (MAPFib). This work has been supported by Ministry of Science, Technological Development and Innovation, Republic of Serbia through Grant Agreement with University of Belgrade, Faculty of Medicine No: 451-03-66/2024-03/200110. This work was funded by the Ministry of Science, Technological Development and Innovation, Republic of Serbia through Grant Agreement with University of Belgrade-Faculty of Pharmacy No: 451-03-47/2023-01/200161. This work was supported by the Wellcome Trust (225021/Z/22/Z). This work was supported by the Swedish Cancer Society (22 2221.Pj.01.H) and Mrs. Berta Kamprad’s Cancer Foundation (FBKS-2022-8-368). This work was supported by the Scientific and Technological Research Council of Turkey- TUBITAK (119S447 and 22AG077). This work was also supported by European Cooperation in Science and Technology (COST) Action CA20117 Mye-InfoBank (www.mye-infobank.eu); COST is supported by the EU Framework Program Horizon Europe.”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.Tertiary Lymphoid Structures (TLS) are organized aggregates of immune cells such as T cells, B cells, and Dendritic Cells (DCs), as well as fibroblasts, formed postnatally in response to signals from cytokines and chemokines. Central to the function of TLS are DCs, professional antigen-presenting cells (APCs) that coordinate the adaptive immune response, and which can be classified into different subsets, with specific functions, and markers. In this article, we review current data on the contribution of different DC subsets to TLS function in cancer and autoimmunity, two opposite sides of the immune response. Different DC subsets can be found in different tumor types, correlating with cancer prognosis. Moreover, DCs are also present in TLS found in autoimmune and inflammatory conditions, contributing to disease development. Broadly, the presence of DCs in TLS appears to be associated with favorable clinical outcomes in cancer while in autoimmune pathologies these cells are associated with unfavorable prognosis. Therefore, it is important to analyze the complex functions of DCs within TLS in order to enhance our fundamental understanding of immune regulation but also as a possible route to create innovative clinical interventions designed for the specific needs of patients with diverse pathological diseases.This work was developed within the scope of projects with references UIDB/04501/2020 and https://doi.org/10.54499/UIDB/04501/2020, UIDP/04501/2020 and https://doi.org/10.54499/UIDP/04501/2020, 2022.03217.PTDC and DOI 10.54499/2022.03217.PTDC, financially supported by national funds (OE), through FCT - Fundação para a Ciência e Tecnologia, I.P./MCTES. This work was also supported by the World Scleroderma Foundation and Edit Busch Stiftung (MAPFib). This work has been supported by Ministry of Science, Technological Development and Innovation, Republic of Serbia through Grant Agreement with University of Belgrade, Faculty of Medicine No: 451-03-66/2024-03/200110. This work was funded by the Ministry of Science, Technological Development and Innovation, Republic of Serbia through Grant Agreement with University of Belgrade-Faculty of Pharmacy No: 451-03-47/2023-01/200161. This work was supported by the Wellcome Trust (225021/Z/22/Z). This work was supported by the Swedish Cancer Society (22 2221.Pj.01.H) and Mrs. Berta Kamprad’s Cancer Foundation (FBKS-2022-8-368). This work was supported by the Scientific and Technological Research Council of Turkey- TUBITAK (119S447 and 22AG077). This work was also supported by European Cooperation in Science and Technology (COST) Action CA20117 Mye-InfoBank (www.mye-infobank.eu); COST is supported by the EU Framework Program Horizon Europe
Context-aware recommender systems for improved SME productivity
Get PDFThis thesis was submitted for the award of Master of Philosophy and was awarded by Brunel University LondonSMEs in the UK are suffering from a productivity gap compared to larger companies and
must find ways to maximise productivity in order to survive. With the widespread
availability of digital tools, there is much choice for SME employees to take advantage of
these to improve productivity. Tools can be adopted to improve a range of tasks and
activities, such as, digital marketing, accounting, communication, etc. As a result,
companies can improve productivity by positively impacting the rate of work, employee
mental wellbeing, customer relationships, operational costs, and more.
However, with the rapid increase in the number of digital tools on the market today, it is
crucial that users are educated adequately on which tools to implement and how to utilise
them.
Context aware recommender systems can effectively learn about a user’s context and
recommend items that would be suited to their needs. However, the context gathering
process is key in determining the output. With this in mind, the research contributes an
ontology-based context model (SMECAOnto) which gathers user context from SME
employees such as, performance, emotions, and demographics. The context model is then
used by proposed SME-CARS to determine a digital tool training intervention for users
based on their needs with the aim of increasing effective adoption, and consequently, SME
productivity.
SMECAOnto is tested against competency questions through querying to test its
effectiveness. The evaluation is promising and contributes a practical solution to the
relatively understudied field of CARS and SME productivity
Optimising resource allocation for computational offloading in a mobile edge environment
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWith the recent albeit limited rollout of the fifth generation of communications,
alongside the widespread adoption of open-source networking solutions
based on SDN and NFV technologies, opportunities to define the architecture
of 5G over its lifetime have become a hot topic in the industry, both
professionally and academically. Despite noticeable advances in bandwidth,
services planned to be integrated deep within the architecture of 5G
technologies such as Mobile Edge Computing are emerging.
The successful allocation of resources is a pivotal component upon which
effective, latency-sensitive handling of data will build on to enhance the future
of communication. This research makes three significant contributions to the
field of Multi-access Edge Computing (MEC). Firstly, it involves testing and
validating various network simulation software to identify the most effective
tools for simulating MEC environments. The efficiency of these simulators is
evaluated to ensure they accurately replicate real-life network scenarios, which
is crucial for constructing precise algorithms and determining simulation
parameters.
Secondly, the study implements a single-layer reinforcement learning (RL)
algorithm within the orchestration module of the simulator to optimize network
resource allocation. The goal of the algorithm is to reduce latency and task
failure rates while increasing efficiency. The RL algorithm is benchmarked
against traditional methods like Round Robin and Greedy algorithms,
demonstrating significant improvements in network service levels and task
success rates.
Lastly, the research develops a multi-layer reinforcement learning algorithm
based on the initial single-layer approach. This advanced algorithm
incorporates replay memory and approximate Q functions within a neural
network, addressing various stages of network infrastructure and leveraging
previously generated Q tables. These enhancements ensure more efficient and
effective network management in MEC environments
An Adaptive SDN-Based Load Balancing Method for Edge/Fog-Based Real-Time Healthcare Systems
Get PDFEdge/fog computing has gained significant popularity as a computing paradigm that facilitates real-time applications, especially in healthcare systems. However, deploying these systems in real-world healthcare scenarios presents technical challenges, among which load balancing is a key concern. Load balancing aims to distribute workloads evenly across multiple nodes in a network to optimize processing and communication efficiency. This article proposes an adaptive load-balancing method that combines the strengths of static and software-defined networking (SDN)-based load balancing algorithms for edge/fog-based healthcare systems. A new algorithm called load balancing of optimal edge-server placement (LB-OESP) is proposed to balance the workload statically in the systems, followed by the presentation of an SDN-based greedy heuristic (SDN-GH) algorithm to manage the data flow dynamically within the network. The LB-OESP algorithm effectively balances workloads while minimizing the number of edge servers required, thereby improving system performance and saving costs. The SDN-GH algorithm leverages the benefits of SDN to dynamically balance the load and provide a more efficient system. Simulation results demonstrate that the proposed method provides an adaptive load-balancing solution that takes into consideration changing network conditions and ensures improved system performance and reliability. Furthermore, the proposed method offers a 12% reduction in system latency and up to 28% lower deployment costs compared to the previous studies. The proposed method is a promising solution for edge/fog-based healthcare systems, providing an efficient and cost-effective approach to managing workloads.Brunel University London, U.K
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