Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review

Pharmacological Inhibition of LRRK2 Exhibits Neuroprotective Activity in Mouse Photothrombotic Stroke Model

Leucine-rich repeat kinase 2 (LRRK2) mutations are the most common cause of Parkinson's disease (PD). Interestingly, recent studies have reported an increased risk of stroke in patients with PD harboring LRRK2 mutations, but there is no evidence showing the functional involvement of LRRK2 in stroke. Here, we found that LRRK2 kinase activity was significantly induced in the Rose-Bengal (RB) photothrombosis-induced stroke mouse model. Interestingly, stroke infarct volumes were significantly reduced, and neurological deficits were diminished by pharmacological inhibition of LRRK2 kinase activity using MLi-2, a brain-penetrant LRRK2 kinase inhibitor. Immunohistochemical analysis showed p-LRRK2 level in stroke lesions, co-localizing with mitophagy-related proteins (PINK, Parkin, LC3B, cytochrome c), suggesting their involvement in stroke progression. Overlapping p-LRRK2 with cytochrome c/TUNEL/JC-1 (an indicator of mitochondrial membrane potential) puncta in RB photothrombosis indicated LRRK2-induced mitochondrial apoptosis, which was blocked by MLi-2. These results suggest that pharmacological inhibition of LRRK2 kinase activity could attenuate mitochondrial apoptosis, ultimately leading to neuroprotective potential in stroke progression. In conclusion, LRRK2 kinase activity might be neuro-pathogenic due to impaired mitophagy in stroke progression, and pharmacological inhibition of LRRK2 kinase activity could be beneficial in reducing the risk of stroke in patients with LRRK2 mutations.

Molecular mechanism of cognitive impairment associated with Parkinson's disease: A stroke perspective

Parkinson's disease (PD) is a common neurological illness that causes several motor and non-motor symptoms, most characteristically limb tremors and bradykinesia. PD is a slowly worsening disease that arises due to progressive neurodegeneration of specific areas of the brain, especially the substantia nigra of the midbrain. Even though PD has continuously been linked to a higher mortality risk in numerous epidemiologic studies, there have been significant discoveries regarding the connection between PD and stroke. The incidence of strokes such as cerebral infarction and hemorrhage is substantially associated with the development of PD. Moreover, cognitive impairments, primarily dementia, have been associated with stroke and PD. However, the underlying molecular mechanism of this phenomenon is still obscure. This concise review focuses on the relationship between stroke and PD, emphasizing the molecular mechanism of cognition deficit and memory loss evident in PD and stroke. Furthermore, we are also highlighting some potential drug molecules that can target both PD and stroke.

SGLT2 inhibitor empagliflozin alleviates cardiac remodeling and contractile anomalies in a FUNDC1-dependent manner in experimental Parkinson's disease

Recent evidence shows a close link between Parkinson's disease (PD) and cardiac dysfunction with limited treatment options. Mitophagy plays a crucial role in the control of mitochondrial quantity, metabolic reprogramming and cell differentiation. Mutation of the mitophagy protein Parkin is directly associated with the onset of PD. Parkin-independent receptor-mediated mitophagy is also documented such as BCL2/adenovirus E1B 19 kDa protein-interacting protein 3 (BNIP3) and FUN14 domain containing 1 (FUNDC1) for receptor-mediated mitophagy. In this study we investigated cardiac function and mitophagy including FUNDC1 in PD patients and mouse models, and evaluated the therapeutic potential of a SGLT2 inhibitor empagliflozin. MPTP-induced PD model was established. PD patients and MPTP mice not only displayed pronounced motor defects, but also low plasma FUNDC1 levels, as well as cardiac ultrastructural and geometric anomalies (cardiac atrophy, interstitial fibrosis), functional anomalies (reduced E/A ratio, fractional shortening, ejection fraction, cardiomyocyte contraction) and mitochondrial injury (ultrastructural damage, UCP2, PGC1α, elevated mitochondrial Ca2+ uptake proteins MCU and VDAC1, and mitochondrial apoptotic protein calpain), dampened autophagy, FUNDC1 mitophagy and apoptosis. By Gene set enrichment analysis (GSEA), we found overtly altered glucose transmembrane transport in the midbrains of MPTP-treated mice. Intriguingly, administration of SGLT2 inhibitor empagliflozin (10 mg/kg, i.p., twice per week for 2 weeks) in MPTP-treated mice significantly ameliorated myocardial anomalies (with exception of VDAC1), but did not reconcile the motor defects or plasma FUNDC1. FUNDC1 global knockout (FUNDC1-/- mice) did not elicit any phenotype on cardiac geometry or function in the absence or presence of MPTP insult, but it nullified empagliflozin-caused cardioprotection against MPTP-induced cardiac anomalies including remodeling (atrophy and fibrosis), contractile dysfunction, Ca2+ homeostasis, mitochondrial (including MCU, mitochondrial Ca2+ overload, calpain, PARP1) and apoptotic anomalies. In neonatal and adult cardiomyocytes, treatment with PD neurotoxin preformed fibrils of α-synuclein (PFF) caused cytochrome c release and cardiomyocyte mechanical defects. These effects were mitigated by empagliflozin (10 μM) or MCU inhibitor Ru360 (10 μM). MCU activator kaempferol (10 μM) or calpain activator dibucaine (500 μM) nullified the empagliflozin-induced beneficial effects. These results suggest that empagliflozin protects against PD-induced cardiac anomalies, likely through FUNDC1-mediated regulation of mitochondrial integrity.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

BACKGROUND AND MOTIVATION

Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias.

METHODS

Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework.

FINDINGS AND CONCLUSIONS

UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Parkinson's disease and cardiovascular involvement: Edifying insights (Review)

Parkinson's disease (PD) is one of the most common neurodegenerative illnesses, and is a major healthcare burden with prodigious consequences on life-quality, morbidity, and survival. Cardiovascular diseases are the leading cause of mortality worldwide and growing evidence frequently reports their co-existence with PD. Cardiac dysautonomia due to autonomic nervous system malfunction is the most prevalent type of cardiovascular manifestation in these patients, comprising orthostatic and postprandial hypotension, along with supine and postural hypertension. Moreover, many studies have endorsed the risk of patients with PD to develop ischemic heart disease, heart failure and even arrhythmias, but the underlying mechanisms are not entirely clear. As importantly, the medication used in treating PD, such as levodopa, dopamine agonists or anticholinergic agents, is also responsible for cardiovascular adverse reactions, but further studies are required to elucidate the underlying mechanisms. The purpose of this review was to provide a comprehensive overview of current available data regarding the overlapping cardiovascular disease in patients with PD.

Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review

OBJECTIVES

To identify ML tools in hospital settings and how they were implemented to inform decision-making for patient care through a scoping review. We investigated the following research questions: What ML interventions have been used to inform decision-making for patient care in hospital settings? What strategies have been used to implement these ML interventions?

DESIGN

A scoping review was undertaken. MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and the Cochrane Database of Systematic Reviews (CDSR) were searched from 2009 until June 2021. Two reviewers screened titles and abstracts, full-text articles, and charted data independently. Conflicts were resolved by another reviewer. Data were summarised descriptively using simple content analysis.

SETTING

Hospital setting.

PARTICIPANT

Any type of clinician caring for any type of patient.

INTERVENTION

Machine learning tools used by clinicians to inform decision-making for patient care, such as AI-based computerised decision support systems or "'model-based'" decision support systems.

PRIMARY AND SECONDARY OUTCOME MEASURES

Patient and study characteristics, as well as intervention characteristics including the type of machine learning tool, implementation strategies, target population. Equity issues were examined with PROGRESS-PLUS criteria.

RESULTS

After screening 17 386 citations and 3474 full-text articles, 20 unique studies and 1 companion report were included. The included articles totalled 82 656 patients and 915 clinicians. Seven studies reported gender and four studies reported PROGRESS-PLUS criteria (race, health insurance, rural/urban). Common implementation strategies for the tools were clinician reminders that integrated ML predictions (44.4%), facilitated relay of clinical information (17.8%) and staff education (15.6%). Common barriers to successful implementation of ML tools were time (11.1%) and reliability (11.1%), and common facilitators were time/efficiency (13.6%) and perceived usefulness (13.6%).

CONCLUSIONS

We found limited evidence related to the implementation of ML tools to assist clinicians with patient healthcare decisions in hospital settings. Future research should examine other approaches to integrating ML into hospital clinician decisions related to patient care, and report on PROGRESS-PLUS items.

FUNDING

Canadian Institutes of Health Research (CIHR) Foundation grant awarded to SES and the CIHR Strategy for Patient Oriented-Research Initiative (GSR-154442). SCOPING REVIEW REGISTRATION: https://osf.io/e2mna.

Stroke in Parkinson's disease: a review of epidemiological studies and potential pathophysiological mechanisms

Parkinson's disease (PD) is the fastest growing neurological disorder and one of the leading neurological causes of disability worldwide following stroke. An overall aging global population, as well as general changes in lifestyle associated with mass industrialization in the last century, may be linked to both increased incidence rates of PD and an increase in cumulative cardiovascular risk. Recent epidemiological studies show an increased risk of stroke, post-stroke complications, and subclinical ischemic insults in PD. PD patients have a host of characteristics that might contribute to increasing the risk of developing ischemic stroke including motor impairment, dysautonomia, and sleep disorders. This increases the urgency to study the interplay between PD and other neurological disorders, and their combined effect on mortality, morbidity, and quality of life. In this review, we provide a comprehensive overview of the studied etiological factors and pathological processes involved in PD, specifically with regard to their relationship to stroke. We hope that this review offers an insight into the relationship between PD and ischemic stroke and motivates further studies in this regard.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives

According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.

Detecting vulnerable carotid plaque and its component characteristics: Progress in related imaging techniques

Carotid atherosclerotic plaque rupture and thrombosis are independent risk factors for acute ischemic cerebrovascular disease. Timely identification of vulnerable plaque can help prevent stroke and provide evidence for clinical treatment. Advanced invasive and non-invasive imaging modalities such as computed tomography, magnetic resonance imaging, intravascular ultrasound, optical coherence tomography, and near-infrared spectroscopy can be employed to image and classify carotid atherosclerotic plaques to provide clinically relevant predictors used for patient risk stratification. This study compares existing clinical imaging methods, and the advantages and limitations of different imaging techniques for identifying vulnerable carotid plaque are reviewed to effectively prevent and treat cerebrovascular diseases.

An Entropy-Based Measure of Complexity: An Application in Lung-Damage

The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.

Intracellular Toxic Advanced Glycation End-Products May Induce Cell Death and Suppress Cardiac Fibroblasts

Cardiovascular disease (CVD) is a lifestyle-related disease (LSRD) induced by the dysfunction and cell death of cardiomyocytes. Cardiac fibroblasts are activated and differentiate in response to specific signals, such as transforming growth factor-β released from injured cardiomyocytes, and are crucial for the protection of cardiomyocytes, cardiac tissue repair, and remodeling. In contrast, cardiac fibroblasts have been shown to induce injury or death of cardiomyocytes and are implicated in the pathogenesis of diseases such as cardiac hypertrophy. We designated glyceraldehyde-derived advanced glycation end-products (AGEs) as toxic AGEs (TAGE) due to their cytotoxicity and association with LSRD. Intracellular TAGE in cardiomyocytes decreased their beating rate and induced cell death in the absence of myocardial ischemia. The TAGE levels in blood were elevated in patients with CVD and were associated with myocardial ischemia along with increased risk of atherosclerosis in vascular endothelial cells in vitro. The relationships between the dysfunction or cell death of cardiac fibroblasts and intracellular and extracellular TAGE, which are secreted from certain organs, remain unclear. We examined the cytotoxicity of intracellular TAGE by a slot blot analysis, and TAGE-modified bovine serum albumin (TAGE-BSA), a model of extracellular TAGE, in normal human cardiac fibroblasts (HCF). Intracellular TAGE induced cell death in normal HCF, whereas TAGE-BSA did not, even at aberrantly high non-physiological levels. Therefore, only intracellular TAGE induced cell death in HCF under physiological conditions, possibly inhibiting the role of HCF.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.