Diagnosis Of PD Research Articles

Vascular disease and risk of fall-related injuries in Parkinson's disease: A nationwide cohort study in Sweden

IntroductionParkinson's disease (PD) patients are prone to fall and fall-related injuries (FI). Vascular disease is common in PD and is positively associated with falls in elderly. We aimed to evaluate the association of vascular disease with FI risk in PD. MethodsA nationwide cohort study of patients with primary PD diagnosis in Sweden was performed using Swedish national registers. Patients with and without vascular disease were followed from PD diagnosis until subsequent FI or 2013-12-31. The association of vascular disease with FI risk was estimated as hazard ratio (HR) and 95 % confidence interval (CI) by Cox regression using attained age as underlying timescale. ResultsWe identified 2734 and 6979 incident FI from 8025 PD patients with and 20,543 without vascular disease, respectively. Overall, vascular disease associated positively with subsequent FI, which was mainly driven by the significant risk elevation within the first 6 months following vascular disease (HR < 0.5year [95 % CI] for PD diagnosed ≤75 years is 1.61 [1.39–1.87] and for PD diagnosed >75 years is 1.48 [1.32–1.65]). Thereafter, the association attenuated to null before it rebounded five years after exposure in PD diagnosed ≤75 years (HR > 5year = 1.26, 95 % CI: 1.10–1.45); whereas for PD diagnosed >75 years, it dropped remarkably and remained non-significant 6 months after exposure. When vascular disease was restricted to stroke, we saw a similar temporal pattern except that the short-term HRs among younger patients were stronger, lasted longer, and declined continuously without rebound. ConclusionsFall prevention is crucial to PD patients immediately after a vascular event.

Evaluation of damage discrimination in dopaminergic neurons using dopamine transporter PET tracer [18F]FECNT-d4

BackgroundParkinson’s disease (PD) is a prevalent neurodegenerative disorder worldwide, diagnosed based on classic symptoms like motor dysfunction and cognitive impairments. With the development of various radioactive ligands, positron emission tomography (PET) imaging combined with specific radiolabelling probes has proven to be effective in aiding clinical PD diagnosis. Among these probes, 2β-Carbomethoxy-3β-(4-chlorophenyl)-8-(2-[18F]-fluoroethyl) nortropane ([18F]FECNT) has been utilized as a PET tracer to image dopamine transporter (DAT) integrity in striatal presynaptic dopaminergic terminals. However, the presence of brain-penetrant radioactive metabolites produced by [18F]FECNT may impact the accuracy of PET imaging. In previous research, we developed 2β-Carbomethoxy-3β-(4-chlorophenyl)-8-(2-[18F]-fluoroethyl-1,1,2,2-d4) nortropane ([18F]FECNT-d4), a deuterated derivative with enhanced stability in plasma and the striatum, along with a slower washout rate. In this study, we further investigated the potential of [18F]FECNT-d4 to detect dopaminergic neuron degeneration in Parkinson’s disease. This involved PET imaging in unilaterally-lesioned PD model rats and in vitro autoradiography conducted on postmortem brain sections.ResultsPET images revealed reduced specific uptake in the ipsilateral striatum of rats stereotactically injected with 6-hydroxydopamine hydrochloride (6-OHDA). Compared to the sham group, the ratio of standardized uptake value (SUV) in the ipsilateral to contralateral striatum decreased by 13%, 23%, and 63% in the mild, moderate, and severe lesioned groups, respectively. Dopaminergic denervation observed in PET imaging was further supported by behavioral assessments, immunostaining, and monoamine concentration tests. Moreover, the microPET results exhibited positive correlations with these measurements, except for the apomorphine-induced rotational behavior test, which showed a negative correlation. Additionally, [18F]FECNT-d4 uptake was approximately 40% lower in the postmortem striatal sections of a PD patient compared to a healthy subject. Furthermore, estimated human dosimetry (effective dose equivalent: 5.06 E-03 mSv/MBq), extrapolated from rat biodistribution data, remained below the current Food and Drug Administration limit for radiation exposure.ConclusionOur findings demonstrate that [18F]FECNT-d4 accurately estimates levels of dopaminergic neuron degeneration in the 6-OHDA-induced PD rat model and effectively distinguishes between PD patients and healthy individuals. This highly sensitive and safe PET probe holds promising potential for clinical application in the diagnosis and monitoring of Parkinson’s disease.

Computerized assessment of handwriting in de novo Parkinson's disease: A kinematic study

IntroductionDysgraphia, a recognized PD motor symptom, lacks effective clinical assessment. Current evaluation relies on motor assessment scales.Computational methods introduced over the past decade offer an objective dysgraphia assessment, considering size, duration, speed, and handwriting fluency. Objective evaluation of dysgraphia may be of help for early diagnosis of PD. ObjectiveComputerized assessment of dysgraphia in de novo PD patients and its correlation with clinical scales. MethodsWe evaluated 38 recently diagnosed, premedication PD patients and age-matched controls without neurological disorders. Participants wrote “La casa de Pamplona es bonita” three times on paper and once on a Wacom tablet under the paper, totaling four phrases. Writing segments of 5–10 s were analyzed. The Wacom tablet captured kinematic data, including mean velocity, mean acceleration, and pen pressure. Data were saved in.svc format and analyzed using specialized software developed by Tecnocampus Mataró. Standard clinical practice data, Hoehn & Yahr staging, and UPDRS scales were used for evaluation. ResultsSignificant kinematic differences existed; patients had lower mean speed (27 ± 12 vs. 48 ± 18, p < 0.0001) and mean acceleration (7.2 ± 3.9 vs. 15.01 ± 7, p < 0.0001) than controls. Mean speed and mean acceleration correlated significantly with UPDRS III scores (speed: r = −0.52, p < 0.0007; acceleration: r = 0.60, p < 0.0001), indicating kinematic parameters' potential in PD evaluation. ConclusionsDysgraphia is identifiable in PD patients, even de novo, indicating an early symptom and correlates with clinical scales, offering potential for objective PD patient evaluation.

Outcomes for children with recurrent/refractory atypical teratoid rhabdoid tumor: A single-institution study with molecular correlation.

Survival data for recurrent pediatric atypical teratoid rhabdoid tumor (ATRT) and its association to molecular groups are extremely limited. Single-institution retrospective study of 64 children less than 21years old with recurrent or treatment-refractory (progressive disease [PD]) ATRT treated at St. Jude Hospital from January 2000 to December 2020. Demographic, clinicopathologic, treatment, molecular grouping (SHH, TYR, and MYC) and germline data were collected. Progression-free survival (PFS2: time from PD to subsequent first progression) and overall survival (OSpostPD: time from PD to death/last follow-up) were estimated by Kaplan-Meier analysis. Median age at and time from initial diagnosis to PD were 2.1years (range: 0.5-17.9years) and 5.4months (range: 0.5-125.6months), respectively. Only five of 64 children (7.8%) are alive at median follow-up of 10.9 (range: 4.2-18.1) years from PD. The 2/5-year PFS2 and OSpostPD were 3.1% (±1.8%)/1.6% (±1.1%) and 20.3% (±4.8%)/7.3% (±3.5%), respectively. Children with TYR group (n=10) had a better OSpostPD compared to those with MYC (n=11) (2-year survival estimates: 60.0%±14.3% vs. 18.2%±9.5%; p=.019), or those with SHH (n=21; 4.8%±3.3%; p=.014). In univariate analyses, OSpostPD was better with older age at diagnosis (p=.037), female gender (p=.008), and metastatic site of PD compared to local or combined sites of PD (p<.001). Two-year OSpostPD for patients receiving any salvage therapy (n=39) post PD was 33.3%±7.3%. Children with recurrent/refractory ATRT have dismal outcomes. Older age at diagnosis, female gender, TYR group, and metastatic site of PD were associated with relatively longer survival in our study.

Ethical Considerations for Identifying Individuals in the Prodromal/Early Phase of Parkinson's Disease: A Narrative Review.

The ability to identify individuals in the prodromal phase of Parkinson's disease has improved in recent years, raising the question of whether and how those affected should be informed about the risk of future disease. Several studies investigated prognostic counselling for individuals with isolated REM sleep behavior disorder and have shown that most patients want to receive information about prognosis, but autonomy and individual preferences must be respected. However, there are still many unanswered questions about risk disclosure or early diagnosis of PD, including the impact on personal circumstances, cultural preferences and specific challenges associated with different profiles of prodromal symptoms, genetic testing or biomarker assessments. This narrative review aims to summarize the current literature on prognostic counselling and risk disclosure in PD, as well as highlight future perspectives that may emerge with the development of new biomarkers and their anticipated impact on the definition of PD.

A minimally Invasive Biomarker for Sensitive and Accurate Diagnosis of Parkinson's Disease.

ImportanceParkinson′s disease (PD), the second most common neurodegenerative disease, is pathologically characterized by intraneuronal deposition of misfolded alpha-synuclein aggregates (αSynD). αSynDseeding activities in CSF and skin samples have shown great promise in PD diagnosis, but they require invasive procedures. Sensitive and accurate αSynDseed amplification assay (αSyn-SAA) for more accessible and minimally invasive samples (such as blood and saliva) are urgently needed for PD pathological diagnosis in routine clinical practice.ObjectiveTo develop a sensitive and accurate αSyn-SAA biomarker using blood and saliva samples for sensitive, accurate and minimally invasive PD diagnosis.Design, Setting, and ParticipantsThis prospective diagnostic study evaluates serum and saliva samples collected from patients clinically diagnosed with PD or healthy controls (HC) without PD at an academic Parkinson′s and Movement Disorders Center from February 2020 to March 2024. Patients diagnosed with non-PD parkinsonism were excluded from this analysis. A total of 124 serum samples (82 PD and 42 HC) and 131 saliva samples (83 PD and 48 HC) were collected and examined by αSyn-SAA. Out of the 124 serum donors, a subset of 74 subjects (48 PD and 26 HC) also donated saliva samples during the same visits. PD patients with serum samples had a mean age of 69.21 years (range 44-88); HC subjects with serum samples had a mean age of 66.55 years (range 44-81); PD patients with saliva samples had a mean age of 69.58 years (range 49-87); HC subjects with saliva samples had a mean age of 64.71 years (range 30-81).Main Outcomes and MeasuresSerum and/or saliva αSynDseeding activities from PD and HC subjects were measured by αSyn-SAA using the Real-Time Quaking-Induced Conversion (RT-QuIC) platform. These PD patients had extensive clinical assessments including MDS-UPDRS. For a subset of PD and HC subjects whose serum and saliva samples were both collected during the same visits, the αSynDseeding activities in both samples from the same subjects were examined, and the diagnostic accuracies for PD based on the seeding activities in either sample alone or both samples together were compared.ResultsRT-QuIC analysis of αSynDseeding activities in the 124 serum samples revealed a sensitivity of 80.49%, a specificity of 90.48%, and an accuracy of 0.9006 (AUC of ROC, 95% CI, 0.8472-0.9539, p&lt;0.0001) for PD diagnosis. RT-QuIC analysis of αSynDseeding activity in 131 saliva samples revealed a sensitivity of 74.70%, a specificity of 97.92%, and an accuracy of 0.8966 (AUC of ROC, 95% CI, 0.8454-0.9478, p&lt;0.0001). When aSynDseeding activities in the paired serum-saliva samples from the subset of 48 PD and 26 HC subjects were considered together, sensitivity was 95.83%, specificity was 96.15%, and the accuracy was 0.98 (AUC of ROC, 95% CI, 0.96-1.00, p&lt;0.001), which are significantly better than when αSynDseeding activities in either serum or saliva were used alone. For the paired serum-saliva samples, when specificity was set at 100% by elevating the αSyn-SAA cutoff values, a sensitivity of 91.7% and an accuracy of 0.9457 were still attained. Detailed correlation analysis revealed that αSynDseeding activities in the serum of PD patients were correlated inversely with Montreal Cognitive Assessment (MoCA) score (p=0.04), positively with Hamilton Depression Rating Scale (HAM-D) (p=0.03), and weakly positively with PDQ-39 cognitive impairment score (p=0.07). Subgroup analysis revealed that the inverse correlation with MoCA was only seen in males (p=0.013) and weakly in the ≥70 age group (p=0.07), and that the positive correlation with HAM-D was only seen in females (p=0.04) and in the &lt;70 age group (p=0.01). In contrast, αSynDseeding activities in the saliva of PD patients were inversely correlated with age at diagnosis (p=0.02) and the REM sleep behavior disorder (RBD) status (p=0.04), but subgroup analysis showed that the inverse correlation with age at diagnosis was only seen in males (p=0.04) and in the &lt;70 age group (p=0.01).Conclusion and RelevanceOur data show that concurrent RT-QuIC assay of αSynDseeding activities in both serum and saliva can achieve high diagnostic accuracies comparable to that of CSF αSyn-SAA, suggesting that αSynDseeding activities in serum and saliva together can potentially be used as a valuable biomarker for highly sensitive, accurate, and minimally invasive diagnosis of PD in routine clinical practice. αSynDseeding activities in serum and saliva of PD patients correlate differentially with some clinical characteristics and in an age and sex-dependent manner.

MiR-142-3p alleviates neuronal apoptosis in Parkinson’s disease via negatively regulating C9orf72

Although microRNA (miRNA) have important clinical prospects in the early diagnosis and treatment of PD, the functions and mechanisms of miRNAs in PD models remain poorly defined. In this study, we screened 9 miRNAs that differently expressed in PD patients and found that miR-142-3p expression was downregulated in both animal and cell models of PD. We showed that overexpression of miR-142-3p significantly alleviates the neuronal damage induced by MPP+, while knockdown of miR-142-3p exacerbates the neuronal damage caused by MPP+. We further found that miR-142-3p targets and inhibits the expression of C9orf72. Knockdown of C9orf72 mitigated neuronal autophagy dysfunction by reducing excessive activation of the AKT/mTOR pathway after MPP+ stimulation, thereby exerted neuroprotective effects. This study reveals that miR-142-3p protects neuron in PD pathogenesis via negatively regulating C9orf72 and enhancing autophagy. Our findings provides an insight into the development of potential biomarkers and therapeutic targets for PD.

A Hybrid Deep Spatiotemporal Attention‐Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals

ABSTRACTParkinson's disease (PD), a severe and progressive neurological illness, affects millions of individuals worldwide. For effective treatment and management of PD, an accurate and early diagnosis is crucial. This study presents a deep learning‐based model for the diagnosis of PD using a resting state electroencephalogram (EEG) signal. The objective of the study is to develop an automated model that can extract complex hidden nonlinear features from EEG and demonstrate its generalizability on unseen data. The model is designed using a hybrid model, consisting of a convolutional neural network (CNN), bidirectional gated recurrent unit (Bi‐GRU), and attention mechanism. The proposed method is evaluated on three public datasets (UC San Diego, PRED‐CT, and University of Iowa [UI] dataset), with one dataset used for training and the other two for evaluation. The proposed model demonstrated remarkable performance, attaining high accuracy scores of 99.4%, 84%, and 73.2% using UC San Diego, PRED‐CT, and UI datasets, respectively. These results justify the effectiveness and robustness of the proposed model across diverse datasets, highlighting its potential for versatile applications in data analysis and prediction tasks. Our proposed hybrid spatiotemporal attention‐based model has been developed with 10‐fold cross‐validation (CV) for UC San Diego dataset and 10‐fold CV and leave‐one‐out cross‐validation (LOOCV) strategies for PRED‐CT and UI datasets. Our results indicate that the proposed PD detection system is accurate and robust. The developed prototype can be used for other neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, and so forth.

MODERN VIEW TO OPTIMIZATION OF DIAGNOSTICS OF PARKINSON'S DISEASE

Parkinsonism is one of the most significant problems of clinical neurology, both due to its high prevalence in the world's populations and due to the significant disability of patients. The work analyzes the diagnosis of Parkinson's disease (as well as other neurodegenerative diseases) at the prodromal stage. A review of methods for preclinical and early clinical diagnosis of PD shows that the study of prodromal markers and criteria for the premotor phase of PD will make it possible in the future to significantly change the course of the disease using neuroprotective therapy at the stage preceding the death of a significant number of dopaminergic neurons of the substantia nigra.

An interpretable deep learning Bayesian optimized random forest framework for the diagnosis of Parkinson's disease in structural magnetic resonance images

AbstractAs one of the fastest‐growing neurological disorders, Parkinson's disease is a neurodegenerative, cardinally motor ailment. Associated with the late onset of symptoms and often misdiagnosed, it inflicts massive life handicaps. To aid in its early detection, the research community is seeking alternative in‐vivo biomarkers, mainly in the field of neuroimaging. This study aimed to build a computer‐aided diagnosis system to distinguish between healthy controls (HC) and early Parkinsonian patients (PD). Our classification paradigm encompasses a Convolutional Neural Network built from scratch to extract discriminatory features, followed by a Bayesian‐optimized Random Forest classifier (CNN_RF). We trained and validated our customized model on the MRI images of 178 PD and 124 HC, taken from the benchmark Parkinson's Progression Markers Initiative (PPMI) dataset, as well as their brain tissue segments and Jacobian determinant maps. The classification was evaluated using seven performance metrics. For visual interpretation, we employed Grad‐CAM to generate saliency maps. Our findings demonstrate the robustness of the proposed method, as it outperformed deep‐tuned ResNet18, VGG16, and SqueezeNet, implemented for comparison. Furthermore, it resulted in 98.9% accuracy, 98.6% specificity, 99.0% precision, 99.5% F1‐score, a 97.7% Matthews correlation coefficient (MCC), and an AUC of 99.6% for the classification of fused gray matter and Jacobian map features. This indicates that the tailored CNN_RF possesses superior adaptability to identify intrinsic characteristics linked to the disease, as highlighted in the salient maps showcasing pertinent regions in the striatum, thalamus, frontal, temporal, and insular cortices for Parkinson's disease classification. Moreover, it offers a single modality and cost‐effective CAD, which could pave the way for a feasible early PD diagnosis. As data availability and AI robustness increase, PD diagnosis will no longer be a challenging task in the future.

Caracterización psicosocial de personas con enfermedad de Parkinson con enfoque de derechos en salud

This psychosocial characterization study proposes the integration of the variable of knowledge of PD patients' rights with aspects related to physical health, physical activity, emotional state, and health care in a sample of 61 people with PD. The psychosocial approach addresses complementary variables to the clinical diagnosis of PD, such as socioeconomic, educational, cultural, and family variables. The rights-based approach is consistent about the effects on these different areas of life and seeks to ensure that health care is comprehensive and contributes to people's well-being, beyond the treatment of symptoms. The instrument used to obtain the data corresponds to a Likert scale designed, piloted, evaluated, and applied to the data analyzed quantitatively and qualitatively. The results show low levels of knowledge of PD patients' rights. The analysis reports a significant correlation between physical health and emotional states (rho=0.421, p&lt;.001 CI95% [0.60, -0.18]), and a significant correlation between physical activity and knowledge of rights (rho=0.347, p&lt;.001, CI95% [0.55, - 0.10]). People with prepaid medicine have higher scores in physical health (M= 85.6, SD= 13.5) and emotional states (M= 16.1, SD= 5.1).

Integrated bioinformatics analysis for exploring potential biomarkers related to Parkinson’s disease progression

BackgroundParkinson’s disease (PD) is a progressive neurodegenerative disease with increasing prevalence. Effective diagnostic markers and therapeutic methods are still lacking. Exploring key molecular markers and mechanisms for PD can help with early diagnosis and treatment improvement.MethodsThree datasets GSE174052, GSE77668, and GSE168496 were obtained from the GEO database to search differentially expressed circRNA (DECs), miRNAs (DEMis), and mRNAs (DEMs). GO and KEGG enrichment analyses, and protein–protein interaction (PPI) network construction were implemented to explore possible actions of DEMs. Hub genes were selected to establish circRNA-related competing endogenous RNA (ceRNA) networks.ResultsThere were 1005 downregulated DECs, 21 upregulated and 21 downregulated DEMis, and 266 upregulated and 234 downregulated DEMs identified. The DEMs were significantly enriched in various PD-associated functions and pathways such as extracellular matrix organization, dopamine synthesis, PI3K-Akt, and calcium signaling pathways. Twenty-one hub genes were screened out, and a PD-related ceRNA regulatory network was constructed containing 31 circRNAs, one miRNA (miR-371a-3p), and one hub gene (KCNJ6).ConclusionWe identified PD-related molecular markers and ceRNA regulatory networks, providing new directions for PD diagnosis and treatment.

Shoulder arthroplasty outcomes in a series of patients diagnosed with Parkinson’s disease: a retrospective analysis

BackgroundParkinson’s disease (PD) is a neurodegenerative disorder that causes resting tremor and rigidity. Total shoulder arthroplasty (TSA) in patients with PD has proven to be a challenging clinical scenario and is associated with a high risk of complications. The purpose of this study is to explore the relationship between patients diagnosed with PD and outcomes following shoulder arthroplasty (SA). MethodsInstitutional records were queried for patients with PD who underwent SA from 2009 to 2020. Patient medical documentation was retrospectively reviewed to assess demographic information, comorbidity status, preoperative and postoperative range of motion measurements, and complications. Mean ± standard deviation was reported for continuous variables. ResultsA total of 17 patients were identified that met the inclusion criteria. The mean age of the cohort was 57.6 ± 10.6 years at the time of PD diagnosis and 68.1 ± 6.9 years at the time of surgery. The mean follow-up was 1.6 ± 1.2 years. The most common indications for surgery were degenerative joint disease, osteoarthritis, rotator cuff arthropathy, proximal humerus fracture, and rotator cuff tear (1). Mean forward elevation improved from 126.6 ± 24.8° to 131.2 ± 27.8°; mean external rotation improved from 29.4 ± 17.3° to 38.8 ± 10.5°. DiscussionSA is an effective method of improving pain and function in PD patients. Patients undergoing anatomic TSA exhibited the most robust functional improvement in this cohort; however, anatomic TSA was associated with postoperative complications and similar functional measurements were achieved at final follow-up in patients who underwent reverse TSA. Thus, significant care must be taken to ensure the correct implant is chosen on an individual basis to maximize the potential for functional improvement.

Developments of possible clinical diagnostic methods for parkinson’s disease: event-related potentials

ABSTRACT In this study, Event-Related Potential (ERP) analyzes were performed to detect cognitive impairments in PD with Deep Brain Stimulation (DBS). A total of 85 volunteers underwent ERP analysis and neuropsychological testing (NPT) to determine cognitive level. In ERP analyses, prolonged latencies were observed in PD groups. However, patients implanted with DBS showed a decrease in latencies, a decrease in symptoms and statistical improvements in both cognitive and attention skills. Considering all these data, ERP results are promising as a noninvasive method that can be used in both disease status and diagnosis of PD.

Skin Biopsy Detection of Phosphorylated α-Synuclein in Patients With Synucleinopathies

Finding a reliable diagnostic biomarker for the disorders collectively known as synucleinopathies (Parkinson disease [PD], dementia with Lewy bodies [DLB], multiple system atrophy [MSA], and pure autonomic failure [PAF]) is an urgent unmet need. Immunohistochemical detection of cutaneous phosphorylated α-synuclein may be a sensitive and specific clinical test for the diagnosis of synucleinopathies. To evaluate the positivity rate of cutaneous α-synuclein deposition in patients with PD, DLB, MSA, and PAF. This blinded, 30-site, cross-sectional study of academic and community-based neurology practices conducted from February 2021 through March 2023 included patients aged 40 to 99 years with a clinical diagnosis of PD, DLB, MSA, or PAF based on clinical consensus criteria and confirmed by an expert review panel and control participants aged 40 to 99 years with no history of examination findings or symptoms suggestive of a synucleinopathy or neurodegenerative disease. All participants completed detailed neurologic examinations and disease-specific questionnaires and underwent skin biopsy for detection of phosphorylated α-synuclein. An expert review panel blinded to pathologic data determined the final participant diagnosis. Skin biopsy for detection of phosphorylated α-synuclein. Rates of detection of cutaneous α-synuclein in patients with PD, MSA, DLB, and PAF and controls without synucleinopathy. Of 428 enrolled participants, 343 were included in the primary analysis (mean [SD] age, 69.5 [9.1] years; 175 [51.0%] male); 223 met the consensus criteria for a synucleinopathy and 120 met criteria as controls after expert panel review. The proportions of individuals with cutaneous phosphorylated α-synuclein detected by skin biopsy were 92.7% (89 of 96) with PD, 98.2% (54 of 55) with MSA, 96.0% (48 of 50) with DLB, and 100% (22 of 22) with PAF; 3.3% (4 of 120) of controls had cutaneous phosphorylated α-synuclein detected. In this cross-sectional study, a high proportion of individuals meeting clinical consensus criteria for PD, DLB, MSA, and PAF had phosphorylated α-synuclein detected by skin biopsy. Further research is needed in unselected clinical populations to externally validate the findings and fully characterize the potential role of skin biopsy detection of phosphorylated α-synuclein in clinical care.

Optical Coherence Tomography as a Biomarker in the Differential Diagnosis between Parkinson’s Disease and Atypical Parkinsonian Syndromes: A Narrative Review

Parkinsonism may be a clinical manifestation of a wide range of disease entities, and still poses a great diagnostic challenge. In an attempt to provide further insight into the differential diagnosis of PD versus progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), and Lewy body dementia (LBD), several biomarkers have been investigated, yielding inconclusive results, OCT being among them. The present review aims to explore the potential diagnostic value of evaluating retinal parameters through OCT implementation among patients presenting with a Parkinsonian syndrome, with an emphasis on effective differentiation between distinct syndromes. Having reviewed all the available literature published within the last decade, neurodegeneration seems to be paralleled with degeneration and alterations of the retina that may be quantified by OCT. Specific patterns of structural changes within the retina may provide valuable information on the underlying pathology, thus highlighting the role of OCT as a diagnostic tool within this group of patients. Although still not utilized in clinical practice, OCT, if further explored and validated, may significantly enhance overall Parkinsonism care.

Exploring Longitudinal MRI-Based Deep Learning Analysis in Parkinson’s Patients - A Short Survey Focus on Handedness

&lt;p class="MsoNormal" style="text-align: justify;"&gt;&lt;span lang="EN-US" style="mso-bidi-font-size: 10.5pt; font-family: Nunito; color: #212529; background: white;"&gt;Parkinson&amp;rsquo;s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.&lt;/span&gt;&lt;/p&gt;

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease.

Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context. Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD. This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter. This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD. The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment

An innovative approach to enhance image alignment through affine transformation, allowing images to be rotated from 0 to 135 degrees. This transformation is a crucial step in improving the diagnostic process, as image misalignment can lead to inaccurate results. The accurate alignment sets the stage for a robust U-Net model, which excels in image segmentation. Precise segmentation is vital for isolating affected brain regions, aiding in the identification of PD-related anomalies. Finally, we introduce the DenseNet architecture model for disease classification, distinguishing between PD and non-PD cases. The combination of these DL models outperforms existing diagnostic approaches in terms of acceptance precision (99.45%), accuracy (99.95%), sensitivity (99.67%), and F1-score (99.84%). In addition, we have developed user-friendly graphical interface software that enables efficient and reasonably accurate class detection via Magnetic Resonance Imaging (MRI). This software exhibits superior efficiency contrasted to current cutting-edges technique, presenting an encouraging opportunity for early disease detection. In summary, our research tackles the problem of low accuracy in existing PD diagnostic models and addresses the critical need for more precise and timely PD diagnoses. By enhancing image alignment and employing advanced DL models, we have achieved substantial improvements in diagnostic accuracy and provided a valuable tool for early PD detection.

Descriptive study of general practitioner's practices and knowledge about Parkinson's disease in the north of France

BackgroundParkinson's disease (PD), the second most frequent neurodegenerative disease, constitutes a major public health challenge. A guide published by the French National Authority for Health in 2012 and revised in 2016 put forward recommendations for general practitioners (GP) planning care pathways for parkinsonian patients. It is well known that PD can be difficult to diagnose, and that when patients consult their GP, symptoms are often still limited and embedded in clinical uncertainty. This means the pathway to confirmed diagnosis of PD can be lengthy and uncertain. Consequently, it is important to identify the difficulties GPs encounter when caring for PD patients in order to help them better close the gaps in care strategies. MethodsWe conducted a descriptive cross-sectional survey in northern France to evaluate GP practices and knowledge about PD and their accordance with care pathway recommendations. The survey was conducted using a 30-item questionnaire sent to a sample of GPs. ResultsThere were 164 GPs who responded to the study questionnaire. The responding GPs generally followed current care pathway recommendations. In presence of a parkinsonian syndrome, 93.3% of the GPs reported systematically looking for an iatrogenic cause; 57.4% did not announce the diagnosis without the advice of a neurologist; 97.6% referred patients to a neurologist when they suspected PD; and 80.5% asked the neurologist to modify treatments. Our findings also revealed some difficult aspects of GP practices: only 2.5% had had additional training in neurology; only 53.6% felt comfortable with the diagnosis of PD; 63.6% prescribed additional exams for the diagnosis; most of the GPs were unaware of second-line treatments and their indications, and finally existence of PD expert centers was unknown for 85.2%. ConclusionsThese findings could be useful to guide implementation of new measures supporting more holistic care for PD patients; PD expert centers in France could provide complementary information and training for GPs.