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Molecular Pain Jun 2024Pain and anxiety are two common and undertreated non-motor symptoms in Parkinson's disease (PD), which affect the life quality of PD patients, and the underlying...
Pain and anxiety are two common and undertreated non-motor symptoms in Parkinson's disease (PD), which affect the life quality of PD patients, and the underlying mechanisms remain unclear. As an important subtype of adenylyl cyclases (ACs), adenylyl cyclase subtype 1 (AC1) is critical for the induction of cortical long-term potentiation (LTP) and injury induced synaptic potentiation in the cortical areas including anterior cingulate cortex (ACC) and insular cortex (IC). Genetic deletion of AC1 or pharmacological inhibition of AC1 improved chronic pain and anxiety in different animal models. In this study, we proved the motor deficit, pain, and anxiety symptoms of PD in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated mice model. As a lead candidate AC1 inhibitor, oral administration (1 dose and 7 doses) of NB001 (20 and 40 mg/kg) showed significant analgesic effect in MPTP-treated mice, and the anxiety behavior was also reduced (40 mg/kg). By using genetic knockout mice, we found that AC1 knockout mice showed reduced pain and anxiety symptoms after MPTP administration, but not AC8 knockout mice. In summary, genetic deletion of AC1 or pharmacological inhibition of AC1 improved pain and anxiety symptoms in PD model mice, but didn't affect motor function. These results suggest that NB001 is a potential drug for the treatment of pain and anxiety symptoms in PD patients by inhibiting AC1 target.
PubMed: 38912637
DOI: 10.1177/17448069241266683 -
Annals of Indian Academy of Neurology May 2024
PubMed: 38912544
DOI: 10.4103/aian.aian_291_24 -
Frontiers in Aging Neuroscience 2024The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using...
OBJECTIVE
The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye.
METHOD
This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).
RESULTS
The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature.
CONCLUSION
The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.
PubMed: 38912523
DOI: 10.3389/fnagi.2024.1393841 -
Bioinformatics Advances 2024Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This...
MOTIVATION
Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.
RESULTS
We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of six isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve interindividual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min. 0.94, Mean 0.98, Max. 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analysing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to nonimmune cells.
AVAILABILITY AND IMPLEMENTATION
EAD workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for single-cell RNA-sequencing data demultiplexing using interindividual variations).
PubMed: 38911824
DOI: 10.1093/bioadv/vbae085 -
Tremor and Other Hyperkinetic Movements... 2024Spinocerebellar ataxia (SCA) denotes an expanding list of autosomal dominant cerebellar ataxias. Although tremor is an important aspect of the clinical spectrum of the... (Review)
Review
BACKGROUND
Spinocerebellar ataxia (SCA) denotes an expanding list of autosomal dominant cerebellar ataxias. Although tremor is an important aspect of the clinical spectrum of the SCAs, its prevalence, phenomenology, and pathophysiology are unknown.
OBJECTIVES
This review aims to describe the various types of tremors seen in the different SCAs, with a discussion on the pathophysiology of the tremors, and the possible treatment modalities.
METHODS
The authors conducted a literature search on PubMed using search terms including tremor and the various SCAs. Relevant articles were included in the review after excluding duplicate publications.
RESULTS
While action (postural and intention) tremors are most frequently associated with SCA, rest and other rare tremors have also been documented. The prevalence and types of tremors vary among the different SCAs. SCA12, common in certain ethnic populations, presents a unique situation, where the tremor is typically the principal manifestation. Clinical manifestations of SCAs may be confused with essential tremor or Parkinson's disease. The pathophysiology of tremors in SCAs predominantly involves the cerebellum and its networks, especially the cerebello-thalamo-cortical circuit. Additionally, connections with the basal ganglia, and striatal dopaminergic dysfunction may have a role. Medical management of tremor is usually guided by the phenomenology and associated clinical features. Deep brain stimulation surgery may be helpful in treatment-resistant tremors.
CONCLUSIONS
Tremor is an elemental component of SCAs, with diverse phenomenology, and emphasizes the role of the cerebellum in tremor. Further studies will be useful to delineate the clinical, pathophysiological, and therapeutic aspects of tremor in SCAs.
Topics: Humans; Tremor; Spinocerebellar Ataxias; Deep Brain Stimulation
PubMed: 38911333
DOI: 10.5334/tohm.911 -
BMC Medical Imaging Jun 2024Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and...
Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.
Topics: Humans; Parkinson Disease; Deep Learning; Tomography, Emission-Computed, Single-Photon; Algorithms; Magnetic Resonance Imaging; Male; Female
PubMed: 38910241
DOI: 10.1186/s12880-024-01335-z -
BMJ Open Jun 2024Biological disease-modifying antirheumatic drugs (bDMARDs) have revolutionised the treatment of inflammatory arthritis (IA). However, many people with IA still require...
PERI-operative biologic DMARD management: Stoppage or COntinuation during orthoPaEdic operations (the PERISCOPE trial) - a study protocol for a pragmatic, UK multicentre, superiority randomised controlled trial with an internal pilot, economic evaluation and nested qualitative study.
INTRODUCTION
Biological disease-modifying antirheumatic drugs (bDMARDs) have revolutionised the treatment of inflammatory arthritis (IA). However, many people with IA still require planned orthopaedic surgery to reduce pain and improve function. Currently, bDMARDs are withheld during the perioperative period due to potential infection risk. However, this predisposes patients to IA flares and loss of disease control. The question of whether to stop or continue bDMARDs in the perioperative period has not been adequately addressed in a randomised controlled trial (RCT).
METHODS AND ANALYSIS
PERISCOPE is a multicentre, superiority, pragmatic RCT investigating the stoppage or continuation of bDMARDs. Participants will be assigned 1:1 to either stop or continue their bDMARDs during the perioperative period. We aim to recruit 394 adult participants with IA. Potential participants will be identified in secondary care hospitals in the UK, screened by a delegated clinician. If eligible and consenting, baseline data will be collected and randomisation completed. The primary outcome will be the self-reported PROMIS-29 (Patient Reported Outcome Measurement Information System) over the first 12 weeks postsurgery. Secondary outcome measures are as follows: PROMIS - Health Assessment Questionnaire (PROMIS-HAQ), EQ-5D-5L, Disease activity: generic global Numeric Rating Scale (patient and clinician), Self-Administered Patient Satisfaction scale, Health care resource use and costs, Medication use, Surgical site infection, delayed wound healing, Adverse events (including systemic infections) and disease-specific outcomes (according to IA diagnosis). The costs associated with stopping and continuing bDMARDs will be assessed. A qualitative study will explore the patients' and clinicians' acceptability and experience of continuation/stoppage of bDMARDs in the perioperative period and the impact postoperatively.
ETHICS AND DISSEMINATION
Ethical approval for this study was received from the West of Scotland Research Ethics Committee on 25 April 2023 (REC Ref: 23/WS/0049). The findings from PERISCOPE will be submitted to peer-reviewed journals and feed directly into practice guidelines for the use of bDMARDs in the perioperative period.
TRIAL REGISTRATION NUMBER
ISRCTN17691638.
Topics: Humans; Orthopedic Procedures; United Kingdom; Antirheumatic Agents; Pragmatic Clinical Trials as Topic; Perioperative Care; Qualitative Research; Multicenter Studies as Topic; Pilot Projects; Cost-Benefit Analysis; Biological Products
PubMed: 38910007
DOI: 10.1136/bmjopen-2024-084997 -
European Journal of Pharmacology Jun 2024Cysteinyl leukotrienes (CysLTs) are central to the pathophysiology of asthma and various inflammatory disorders. Leukotriene receptor antagonists (LTRAs) effectively... (Review)
Review
Cysteinyl leukotrienes (CysLTs) are central to the pathophysiology of asthma and various inflammatory disorders. Leukotriene receptor antagonists (LTRAs) effectively treat respiratory conditions by targeting cysteinyl leukotriene receptors, CysLT and CysLT subtypes. This review explores the multifaceted effects of LTs, extending beyond bronchoconstriction. CysLT receptors are not only present in the respiratory system but are also crucial in neuronal signaling pathways. LTRAs modulate these receptors, influencing downstream signaling, calcium levels, inflammation, and oxidative stress (OS) within neurons hinting at broader implications. Recent studies identify novel molecular targets, sparking interest in repurposing LTRAs for therapeutic use. Clinical trials are investigating their potential in neuroinflammation control, particularly in Alzheimer's disease (AD) and Parkinson's diseases (PD). However, montelukast, a long-standing LTRA since 1998, raises concerns due to neuropsychiatric adverse drug reactions (ADRs). Despite widespread use, understanding montelukast's metabolism and underlying ADR mechanisms remains limited. This review comprehensively examines LTRAs' diverse biological effects, emphasizing non-bronchoconstrictive activities. It also analyses plausible mechanisms behind LTRAs' neuronal effects, offering insights into their potential as neurodegenerative disease modulators. The aim is to inform clinicians, researchers, and pharmaceutical developers about LTRAs' expanding roles, particularly in neuroinflammation control and their promising repurposing for neurodegenerative disease management.
PubMed: 38909933
DOI: 10.1016/j.ejphar.2024.176755 -
Parkinsonism & Related Disorders May 2024Botulinum toxin (BoNT) is first-line treatment for cervical dystonia (CD). Treatment of CD with BoNT usually requires injections every 3-4 months for as long as symptoms... (Review)
Review
INTRODUCTION
Botulinum toxin (BoNT) is first-line treatment for cervical dystonia (CD). Treatment of CD with BoNT usually requires injections every 3-4 months for as long as symptoms persist, which can be for the lifetime of the individual. Duration of BoNT effect can impact quality of life since it is important that efficacy is maintained throughout an injection cycle to avoid fluctuations of effect after each injection. There is currently no consensus on how to assess duration of BoNT effect in patients with CD.
METHODS
A scoping review was conducted to summarize the available evidence from phase 3 clinical trials of BoNT in CD and on the interpretation of the reported duration of effect. The available evidence was analyzed in the context of clinical experience and real-world treatment practices of CD.
RESULTS
Methods for estimating duration of effect varied across publications; most were based on artificial constructs developed for clinical trials (time until a pre-specified efficacy endpoint was reached) and are not appropriate to apply in clinical practice. Clinical trial outcomes in CD were not objectively evaluated, and did not prioritize patients' needs or focus on factors that impact patients' daily living activities and quality of life.
CONCLUSION
Better evidence and consistency of reporting for duration of effect for BoNT in CD is needed to help guide clinicians on when reinjection is likely to be required. The goal should be to keep patients as symptom-free as possible with flexible reinjection intervals tailored to individual needs.
PubMed: 38909588
DOI: 10.1016/j.parkreldis.2024.107011 -
Artificial Intelligence in Medicine Jun 2024Parkinson's Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease... (Review)
Review
BACKGROUND
Parkinson's Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease progression. Advancements in hand tracking using Deep Learning (DL) allow for the automatic and objective disease evaluation from video recordings of standardised motor tasks, which are the foundation of neurological examinations. In view of this scenario, this narrative review aims to describe the state of the art and the future perspective of DL frameworks for hand tracking in video-based PD assessment.
METHODS
A rigorous search of PubMed, Web of Science, IEEE Explorer, and Scopus until October 2023 using primary keywords such as parkinson, hand tracking, and deep learning was performed to select eligible by focusing on video-based PD assessment through DL-driven hand tracking frameworks RESULTS:: After accurate screening, 23 publications met the selection criteria. These studies used various solutions, from well-established pose estimation frameworks, like OpenPose and MediaPipe, to custom deep architectures designed to accurately track hand and finger movements and extract relevant disease features. Estimated hand tracking data were then used to differentiate PD patients from healthy individuals, characterise symptoms such as tremors and bradykinesia, or regress the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) by automatically assessing clinical tasks such as finger tapping, hand movements, and pronation-supination.
CONCLUSIONS
DL-driven hand tracking holds promise for PD assessment, offering precise, objective measurements for early diagnosis and monitoring, especially in a telemedicine scenario. However, to ensure clinical acceptance, standardisation and validation are crucial. Future research should prioritise large open datasets, rigorous validation on patients, and the investigation of new frontiers such as tracking hand-hand and hand-object interactions for daily-life tasks assessment.
PubMed: 38909431
DOI: 10.1016/j.artmed.2024.102914