Challenges in the diagnosis of Parkinson's disease

The phonation test can distinguish the patient with Parkinson's disease via Bayes inference

Parkinson's disease (PD) is a neurodegenerative disease with various clinical manifestations caused by multiple risk factors. However, the effect of different factors and relationships between different features related to PD and the extent of those factors leading to the incidence of PD remains unclear. we employed Bayesian network to construct a prediction model. The prediction system was trained on the data of 35 patients and 26 controls. The structure learning and parameter learning of Bayesian Network was completed through the tree-augmented network (TAN) and Netica software, respectively. We employed four Bayesian Networks in terms of the syllable, including monosyllables, disyllables, multisyllables and unsegmented syllables. The area under the curve (AUC) of monosyllabic, disyllabic, multisyllabic, and unsegmented-syllable models were 0.95, 0.83, 0.80 and 0.84, respectively. In the monosyllabic tests, the best predictor of PD was duration, the posterior probability of which was 92.70%. Meanwhile, minimum f0 (61.60%) predicted best in the disyllabic tests and the variables that predicted best in multisyllables and unsegmented syllables were end f0 (59.40%) and maximum f0 (58.40%). In the cross-sectional comparison, the prediction effect of each variable in the monosyllabic tests was generally higher than that of other test groups. The monosyllabic models had the highest predicted performance of PD. Among acoustic parameters, duration was the strongest feature in predicting the prevalence of PD in monosyllabic tests. We believe that this network methodology will be a useful tool for the clinical prediction of Parkinson's disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10194-x.

Role of the globus pallidus in motor and non-motor symptoms of Parkinson's disease

The globus pallidus plays a pivotal role in the basal ganglia circuit. Parkinson's disease is characterized by degeneration of dopamine-producing cells in the substantia nigra, which leads to dopamine deficiency in the brain that subsequently manifests as various motor and non-motor symptoms. This review aims to summarize the involvement of the globus pallidus in both motor and non-motor manifestations of Parkinson's disease. The firing activities of parvalbumin neurons in the medial globus pallidus, including both the firing rate and pattern, exhibit strong correlations with the bradykinesia and rigidity associated with Parkinson's disease. Increased beta oscillations, which are highly correlated with bradykinesia and rigidity, are regulated by the lateral globus pallidus. Furthermore, bradykinesia and rigidity are strongly linked to the loss of dopaminergic projections within the cortical-basal ganglia-thalamocortical loop. Resting tremors are attributed to the transmission of pathological signals from the basal ganglia through the motor cortex to the cerebellum-ventral intermediate nucleus circuit. The cortico-striato-pallidal loop is responsible for mediating pallidi-associated sleep disorders. Medication and deep brain stimulation are the primary therapeutic strategies addressing the globus pallidus in Parkinson's disease. Medication is the primary treatment for motor symptoms in the early stages of Parkinson's disease, while deep brain stimulation has been clinically proven to be effective in alleviating symptoms in patients with advanced Parkinson's disease, particularly for the movement disorders caused by levodopa. Deep brain stimulation targeting the globus pallidus internus can improve motor function in patients with tremor-dominant and non-tremor-dominant Parkinson's disease, while deep brain stimulation targeting the globus pallidus externus can alter the temporal pattern of neural activity throughout the basal ganglia-thalamus network. Therefore, the composition of the globus pallidus neurons, the neurotransmitters that act on them, their electrical activity, and the neural circuits they form can guide the search for new multi-target drugs to treat Parkinson's disease in clinical practice. Examining the potential intra-nuclear and neural circuit mechanisms of deep brain stimulation associated with the globus pallidus can facilitate the management of both motor and non-motor symptoms while minimizing the side effects caused by deep brain stimulation.

Multimodal magnetic resonance imaging studies on non-motor symptoms of Parkinson's disease

Objective

This study aims to investigate the diagnostic value of multi-modal magnetic resonance imaging (MRI) utilizing arterial spin labeling (ASL), quantitative susceptibility mapping (QSM), and 3D T1-weighted imaging (3DT1WI) in patients with Parkinson's disease (PD). Additionally, it evaluates the relationship between MRI findings and non-motor symptoms associated with PD.

Methods

ASL, QSM, and 3DT1WI scans were performed on 48 PD patients and 46 healthy controls (HC). We extracted and analyzed differences in regional cerebral blood flow (rCBF), magnetic susceptibility, and gray matter density parameters between the two groups. These MRI parameters were correlated with clinical scale scores assessing non-motor symptoms, including cognitive function, sleep quality, olfaction, autonomic function, anxiety, depression, and fatigue. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic accuracy of each imaging modality in distinguishing PD from HC.

Results

The areas under the ROC curve (AUC) for rCBF, magnetic susceptibility, and gray matter density were 0.941, 0.979, and 0.624, respectively. In PD patients, a negative correlation was found between Unified Parkinson's Disease Rating Scale Part II (UPDRS II) scores and rCBF in the bilateral precuneus. The Pittsburgh Sleep Quality Index (PSQI) scores negatively correlated with rCBF in the left middle temporal gyrus and right middle occipital gyrus. Hamilton Depression Rating Scale (HAMD) scores positively correlated with QSM values in the right supplementary motor area, while scores on the Argentine Smell Identification Test (AHRS) negatively correlated with QSM values in the same area. Disease duration showed a positive correlation with QSM values in the right middle cingulate gyrus. Additionally, PSQI scores positively correlated with QSM values in the left middle cingulate gyrus, and fatigue severity scale (FSS) scores also positively correlated with QSM values in the left middle cingulate gyrus. Gray matter atrophy in the left inferior temporal gyrus was associated with cognitive impairment in PD.

Conclusion

Occipital hypoperfusion and cortical atrophy in the left inferior temporal gyrus may serve as novel imaging biomarkers for PD and are associated with sleep disturbances and cognitive impairment in PD patients. Extensive iron deposition in the bilateral cerebral cortex of PD patients may be a contributing factor to non-motor symptoms such as sleep disturbances and fatigue. Multimodal imaging techniques, including ASL, QSM, and 3DT1WI, can enhance the diagnostic accuracy for PD.

Utilization of nanotechnology to surmount the blood-brain barrier in disorders of the central nervous system

Central nervous system (CNS) diseases are a major cause of disability and death worldwide. Due to the blood-brain barrier (BBB), drug delivery for CNS diseases is extremely challenging. Nano-delivery systems can overcome the limitations of BBB to deliver drugs to the CNS, improve the ability of drugs to target the brain and provide potential therapeutic methods for CNS diseases. At the same time, the choice of different drug delivery methods (bypassing BBB or crossing BBB) can further optimize the therapeutic effect of the nano-drug delivery system. This article reviews the different methods of nano-delivery systems to overcome the way BBB enters the brain. Different kinds of nanoparticles to overcome BBB were discussed in depth.

Netrin-1 signaling pathway mechanisms in neurodegenerative diseases

Netrin-1 and its receptors play crucial roles in inducing axonal growth and neuronal migration during neuronal development. Their profound impacts then extend into adulthood to encompass the maintenance of neuronal survival and synaptic function. Increasing amounts of evidence highlight several key points: (1) Diminished Netrin-1 levels exacerbate pathological progression in animal models of Alzheimer's disease and Parkinson's disease, and potentially, similar alterations occur in humans. (2) Genetic mutations of Netrin-1 receptors increase an individuals' susceptibility to neurodegenerative disorders. (3) Therapeutic approaches targeting Netrin-1 and its receptors offer the benefits of enhancing memory and motor function. (4) Netrin-1 and its receptors show genetic and epigenetic alterations in a variety of cancers. These findings provide compelling evidence that Netrin-1 and its receptors are crucial targets in neurodegenerative diseases. Through a comprehensive review of Netrin-1 signaling pathways, our objective is to uncover potential therapeutic avenues for neurodegenerative disorders.

Identification and validation of Atp5f1c in CD4+ T cell as a hub protein in Parkinson's disease

Parkinson's disease (PD) is an age-related and progressive neurodegenerative disease. Growing evidences indicate that CD4+ T cell dysfunction plays an essential role in the progress of PD. Here, in LPS-induced PD mice, we isolated midbrain CD4+ T cell and peripheral CD4+ T cell to perform proteomics, and then screened a total of 167 co-expression proteins via integrated bioinformatics analysis. In addition, the subcellular localization, GO analysis, KEGG pathways and protein-protein interaction of 167 co-expression proteins were assessed. Furthermore, GeneMANIA searched the hub proteins and their co-expression genes and found 13 overlapping hub proteins, including Ndufa3, Cox5b, Mrpl21, Ndufab1, Idh3g, Ndufb7, Cyc1, Cisd1, Atp5f1c, Sdhc, Ndufb9, Mtnd1 and Mrpl17. Next, GO analysis and KEGG analysis of the 13 overlapping hub proteins were also exhibited. Further analysis identified that 4 hub proteins (Idh3g, Cisd1, Atp5f1c and Mtnd1) were downregulated both in midbrain and peripheral CD4+ T cell from proteomics. Identification and rescue experiment analysis showed that only Atp5f1c was decreased in LPS- and 6-OHDA-induced PD mice and dopamine (DA) neuronal loss and ATP production decrease were disappeared after Atp5f1c over-expression/Atp5f1c reinfusion both in vivo and in vitro. In conclusion, Atp5f1c was verified as a potential CD4+ T cell-related hub protein for PD.

Salsolinol as an RNA m6A methylation inducer mediates dopaminergic neuronal death by regulating YAP1 and autophagy

JOURNAL/nrgr/04.03/01300535-202503000-00032/figure1/v/2024-06-17T092413Z/r/image-tiff Salsolinol (1-methyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline, Sal) is a catechol isoquinoline that causes neurotoxicity and shares structural similarity with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, an environmental toxin that causes Parkinson's disease. However, the mechanism by which Sal mediates dopaminergic neuronal death remains unclear. In this study, we found that Sal significantly enhanced the global level of N6-methyladenosine (m6A) RNA methylation in PC12 cells, mainly by inducing the downregulation of the expression of m6A demethylases fat mass and obesity-associated protein (FTO) and alkB homolog 5 (ALKBH5). RNA sequencing analysis showed that Sal downregulated the Hippo signaling pathway. The m6A reader YTH domain-containing family protein 2 (YTHDF2) promoted the degradation of m6A-containing Yes-associated protein 1 (YAP1) mRNA, which is a downstream key effector in the Hippo signaling pathway. Additionally, downregulation of YAP1 promoted autophagy, indicating that the mutual regulation between YAP1 and autophagy can lead to neurotoxicity. These findings reveal the role of Sal on m6A RNA methylation and suggest that Sal may act as an RNA methylation inducer mediating dopaminergic neuronal death through YAP1 and autophagy. Our results provide greater insights into the neurotoxic effects of catechol isoquinolines compared with other studies and may be a reference for assessing the involvement of RNA methylation in the pathogenesis of Parkinson's disease.

Cross-modality PET image synthesis for Parkinson's Disease diagnosis: a leap from [18F]FDG to [11C]CFT

PURPOSE

Dopamine transporter [11C]CFT PET is highly effective for diagnosing Parkinson's Disease (PD), whereas it is not widely available in most hospitals. To develop a deep learning framework to synthesize [11C]CFT PET images from real [18F]FDG PET images and leverage their cross-modal correlation to distinguish PD from normal control (NC).

METHODS

We developed a deep learning framework to synthesize [11C]CFT PET images from real [18F]FDG PET images, and leveraged their cross-modal correlation to distinguish PD from NC. A total of 604 participants (274 with PD and 330 with NC) who underwent [11C]CFT and [18F]FDG PET scans were included. The quality of the synthetic [11C]CFT PET images was evaluated through quantitative comparison with the ground-truth images and radiologist visual assessment. The evaluations of PD diagnosis performance were conducted using biomarker-based quantitative analyses (using striatal binding ratios from synthetic [11C]CFT PET images) and the proposed PD classifier (incorporating both real [18F]FDG and synthetic [11C]CFT PET images).

RESULTS

Visualization result shows that the synthetic [11C]CFT PET images resemble the real ones with no significant differences visible in the error maps. Quantitative evaluation demonstrated that synthetic [11C]CFT PET images exhibited a high peak signal-to-noise ratio (PSNR: 25.0-28.0) and structural similarity (SSIM: 0.87-0.96) across different unilateral striatal subregions. The radiologists achieved a diagnostic accuracy of 91.9% (± 2.02%) based on synthetic [11C]CFT PET images, while biomarker-based quantitative analysis of the posterior putamen yielded an AUC of 0.912 (95% CI, 0.889-0.936), and the proposed PD Classifier achieved an AUC of 0.937 (95% CI, 0.916-0.957).

CONCLUSION

By bridging the gap between [18F]FDG and [11C]CFT, our deep learning framework can significantly enhance PD diagnosis without the need for [11C]CFT tracers, thereby expanding the reach of advanced diagnostic tools to clinical settings where [11C]CFT PET imaging is inaccessible.

Identification of novel phenolic inhibitors from traditional Chinese medicine against toxic α-synuclein aggregation via regulating phase separation

Parkinson's disease (PD), a neurodegenerative disorder without cure, is characterized by the pathological aggregation of α-synuclein (α-Syn) in Lewy bodies. Classic deposition pathway and condensation pathway contribute to α-Syn aggregation, and liquid-liquid phase separation is the driving force for condensate formation, which subsequently undergo liquid-solid phase separation to form toxic fibrils. Traditional Chinese Medicine (TCM) has a long history in treating neurodegenerative disease; herein, we identified chemicals from herbs that inhibit α-Syn aggregation. We screened commonly prescribed TCMs for PD from the CNKI database and registered patents, 13 chemicals were identified in the TCMSP databases as candidate inhibitors, among which three phenols, forsythoside B (FTSB), echinacoside (ECH), and 4-hydroxyindole (C4-OH) efficiently inhibit α-Syn aggregation. Moreover, FTSB and ECH increase α-Syn fluidity within condensates, inhibit α-Syn transition into amyloid fibrils and reduce fibril-induced toxicity in SH-SY5Y cells. Importantly, they disaggregated preformed α-Syn amyloid fibrils. Notably, in an α-Syn overexpressing NL5901 C. elegans PD model, either FTSB or ECH treatment significantly extended the lifespan and improved the PD-like movement disorders, both in the preventive and therapeutic treatment approaches, by reducing toxic α-Syn inclusion formation and improving the fluidity of α-Syn. Together, we offer new therapeutic candidates targeting phase separation-associated aggregation for PD.

PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease

BACKGROUND

Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more effective prediction methods based on artificial intelligence (AI) are needed to assist physicians in timely diagnosis.

NEW METHODS

An explainable deep learning-based early Parkinson's disease diagnostic model, Parkinson's Integrative Diagnostic Gated Network (PIDGN), was designed by fusing Single Nucleotide Polymorphism (SNP) and brain sMRI data. Firstly, unimodal internal information was extracted using EmsembleTree dimensionality reduction method, Transformer encoder and 3D ResNet. Secondly, gated attention fusion technique was utilized to explore the inter-modal interactions. Finally, the classification results were output through the fully connected layer. SHapley additive interpretation (SHAP) values and Gradient-weighted Class Activation Mapping (Grad-CAM) techniques were used to help explain the importance of SNPs and brain regions for PD.

RESULTS

The results showed that the PIDGN model achieved the best results with the accuracy of 0.858 and AUROC of 0.897. Top 20 SNPs and the brain regions near the midbrain potentially related to PD were identified using two explainable techniques via SHAP values and Grad-CAM respectively.

COMPARISON WITH EXISTING METHODS AND CONCLUSION

The PIDGN model trained by fusing genetic and imaging data outperforms 13 other commonly used unimodal or bimodal models. Explainable PIDGN model helps deepen understanding of several SNPs and sMRI key factors that may affect PD. This study provides a potentially effective solution for automated early diagnosis of PD using AI.

Intermittent fasting and neurodegenerative diseases: Molecular mechanisms and therapeutic potential

Neurodegenerative disorders are straining public health worldwide. During neurodegenerative disease progression, aberrant neuronal network activity, bioenergetic impairment, adaptive neural plasticity impairment, dysregulation of neuronal Ca2+ homeostasis, oxidative stress, and immune inflammation manifest as characteristic pathological changes in the cellular milieu of the brain. There is no drug for the treatment of neurodegenerative disorders, and therefore, strategies/treatments for the prevention or treatment of neurodegenerative disorders are urgently needed. Intermittent fasting (IF) is characterized as an eating pattern that alternates between periods of fasting and eating, requiring fasting durations that vary depending on the specific protocol implemented. During IF, depletion of liver glycogen stores leads to the production of ketone bodies from fatty acids derived from adipocytes, thereby inducing an altered metabolic state accompanied by cellular and molecular adaptive responses within neural networks in the brain. At the cellular level, adaptive responses can promote the generation of synapses and neurons. At the molecular level, IF triggers the activation of associated transcription factors, thereby eliciting the expression of protective proteins. Consequently, this regulatory process governs central and peripheral metabolism, oxidative stress, inflammation, mitochondrial function, autophagy, and the gut microbiota, all of which contribute to the amelioration of neurodegenerative disorders. Emerging evidence suggests that weight regulation significantly contributes to the neuroprotective effects of IF. By alleviating obesity-related factors such as blood-brain barrier dysfunction, neuroinflammation, and β-amyloid accumulation, IF enhances metabolic flexibility and insulin sensitivity, further supporting its potential in mitigating neurodegenerative disorders. The present review summarizes animal and human studies investigating the role and underlying mechanisms of IF in physiology and pathology, with an emphasis on its therapeutic potential. Furthermore, we provide an overview of the cellular and molecular mechanisms involved in regulating brain energy metabolism through IF, highlighting its potential applications in neurodegenerative disorders. Ultimately, our findings offer novel insights into the preventive and therapeutic applications of IF for neurodegenerative disorders.

Enhancing drug bioavailability for Parkinson's disease: The promise of chitosan delivery mechanisms

Parkinson's disease (PD) is a widely seen neurodegenerative condition recognized by misfolded α-synuclein (αSyn) protein, a prominent indicator for PD and other synucleinopathies. Motor symptoms like stiffness, akinesia, rest tremor, and postural instability coexist with nonmotor symptoms that differ from person to person in the development of PD. These symptoms arise from a progressive loss of synapses and neurons, leading to a widespread degenerative process in multiple organs. Implementing medical and surgical interventions, such as deep brain stimulation, has enhanced individuals' overall well-being and long-term survival with PD. It should be mentioned that these treatments cannot stop the condition from getting worse. The complicated structure of the brain and the existence of a semi-permeable barrier, commonly known as the BBB, have traditionally made medication delivery for the treatment of PD a challenging endeavor. The drug's low lipophilic nature, enormous size, and peculiarity for various ATP-dependent transport mechanisms hinder its ability to enter brain cells. This article delves at the potential of drug delivery systems based on chitosan (CS) to treat PD.

Classification of Parkinson's disease with and without dopaminergic deficiency based on non-motor symptoms and structural neuroimaging

The presence of non-motor symptoms (NMS) such as olfactive deficit or neuropsychiatric symptoms has been associated with the diagnosis of Parkinson's Disease (PD). NMS are also associated with different brain structural features underlying distinctive processes in PD. NMS has been poorly studied in patients with a PD-like clinical profile, showing Scans Without Evidence of Dopaminergic Deficit (SWEDD). This study proposes to compare classification models differentiating PD, SWEDD and Healthy Controls (HC) based on NMS and neurostructural factors. 683 participants (382 PD diagnosed in the last 2 years, 48 with SWEDD, 170 HC) from the PPMI dataset were compared based on available assessments. Each participant underwent an olfactive, neuropsychiatric and sleep assessment, and a 3T MRI. Brain volumes were extracted and standardized from each MRI. Classifications were based on logistic regressions using 5-fold cross-validation models combining different NMS and MRI data and determining their involvement in differentiation between patient subgroups (PD vs. SWEDD) or between patients and HC. NMS were significant factors in PD vs. SWEDD, PD vs. HC and SWEDD vs. HC classifiers, when considered alone or in combination with MRI data. No classification models were significantly different from chance based-on MRI, nor more accurate combining NMS and MRI when compared with models based on NMS only. These results highlight the importance of NMS in differentiating between PD and SWEDD, PD and HC, SWEDD and HC. However, classical imaging data such as cortical and subcortical volumetry seems insufficient to improve these classifications. Other imaging features such as connectivity could also be studied.

A single-cell transcriptome analysis reveals astrocyte heterogeneity and identifies CHI3L1 as a diagnostic biomarker in Parkinson's disease

Background

Parkinson's disease (PD) is the second most common neurodegenerative disease, characterized by motor and non-motor symptoms. It has been reported that astrocytes play a critical role in the pathogenesis and progression of PD. Here, we aimed to identify the heterogeneity of astrocytes and investigate genes associated with astrocyte differentiation trajectories in PD.

Methods

The single-cell transcriptomic profiles of PD samples were collected from the GEO database. We have identified subsets of astrocytes and analyzed their functions. The differentiation trajectory of astrocyte subtypes was explored using Monocle2. Inflammatory response scores were determined using AUCell. The levels of CHI3L1 mRNA and protein expressions in astrocytes were analyzed using qRT-PCR and Western Blot assay, respectively.

Results

We characterized seven cell types within the substantia nigra region of both PD and normal samples. Our analysis revealed that astrocytes comprised the second-highest proportion of cell types. Additionally, we identified three distinct subpopulations of astrocytes: Astro-C0, Astro-C1, and Astro-C2. Notably, Astro-C0 was associated with inflammatory signaling pathways. Trajectory analysis indicated that Astro-C0 occupies an intermediate stage of differentiation. The astrocyte-related gene CHI3L1 was found to be highly expressed in the Astro-C0 subpopulation. Furthermore, we observed increased levels of CHI3L1 mRNA and protein in LPS-induced astrocytes. Astrocytes exhibiting elevated CHI3L1 levels demonstrated interactions with microglia in PD patients. Lastly, we discovered that CHI3L1 was significantly overexpressed in PD patients and exhibited strong diagnostic potential for the disease.

Conclusion

This study clarified the heterogeneity of astrocytes in PD based on the single-cell transcriptomic profiles and found that CHI3L1 may be a diagnostic biomarker for PD.

Deciphering the role of neuropeptides as biomarkers for early diagnosis of Parkinson's disease

Parkinson's disease (PD) is a neurological condition and is characterized by both motor and non-motor symptoms. Early diagnosis is essential for effective therapy and management; nevertheless, present diagnostic methods are frequently insufficient and primarily rely on clinical symptoms that appear later in the disease. Neuropeptides, such as alpha-synuclein (α-syn), Substance P (SP), neurotensin (Nts), Neuropeptide Y (NPY), and somatostatin (SST), exhibit significant potential as biomarkers for the early identification of Parkinson's disease (PD). The pathophysiology of Parkinson's disease is closely associated with the dysregulation of these neuropeptides, which are essential in many neurophysiological processes. Advancements in detection technologies, including the Enzyme-Linked Immunosorbent Assay (ELISA), have rendered it possible to precisely and sensitively quantify neuropeptides in a variety of bodily fluids, including blood, saliva, tears, urine, and cerebrospinal fluid (CSF). Studies show that PD patients have different amounts of neuropeptides in their biological fluids. These differences are correlated with the severity of the disease and help to distinguish PD patients apart from individuals with other neurodegenerative conditions. Despite being less investigated, Nts and SST are also involved in neuroprotection and dopaminergic transmission, they too hold significant characteristics as diagnostic biomarkers. This article highlights the possible use of neuropeptides as PD diagnostic biomarkers. Integrating neuropeptide biomarkers into normal diagnostic processes can substantially enhance early diagnosis. This enables early therapeutic interventions and improves outcomes for individuals with PD.

Levodopa-synergistic CBT intervention improves Parkinson's disease with anxiety disorder by regulating the BDNF/PI3K/AKT pathway

BACKGROUND

Anxiety disorder is one of the most common and disabling neuropsychiatric syndromes in patients with Parkinson's disease (PD), seriously affecting the quality of life and prognosis of PD patients.

OBJECTIVE

The objective of this study was to analyze the risk factors for anxiety in PD patients and to evaluate the effectiveness of cognitive behavioral therapy (CBT) in treating PD with anxiety disorder (PDAD).

METHODS

Baseline data were recorded for 211 PD patients and 139 PDAD patients, and multi-factorial and independent risk factors for anxiety disorder in PD patients were analyzed. ​The 139 PDAD patients were divided into clinical testing (CMO) and CBT groups. Assessments were taken at baseline and after the end of the intervention. A 5-month follow-up survey was conducted after the intervention. The mouse PD model was induced by MPTP, and the anxiety state of mice was detected by rotarod test and open-field test. The expression of BDNF/PI3K/Akt protein in serum and mouse brain was detected by western blot.

RESULTS

PDAD patients had significantly higher HAMA scores than PD patients. PSQI, ESS, HAMD, SCOPA-AUT, UPDRS-III and Hoehn-Yahr were independent risk factors for anxiety disorder in PD patients. After the intervention, the psychological state, cognitive function and quality of life improved in both the CMO and CBT groups, with the CBT group showing better improvement Results from follow-up showed that the number and frequency of falls was lower in the CBT group than in the CMO group, and that patients were more satisfied with the CBT intervention than the CMO group. L-dopa treatment alleviated anxiety in PD mice. L-dopa treatment increased BDNF, p-PI3K, and p-Akt protein levels. Moreover, the combination of L-dopa and CBT enhanced the boosting effect of L-dopa on these proteins.

CONCLUSION

CBT is an effective treatment for anxiety in patients with Parkinson's disease. Medications combined with CBT have been shown to be effective in improving depression, anxiety and quality of life in PDAD patients.

Mapping reactive astrogliosis in Parkinson's brain with astroglial tracers BU99008 and Deprenyl: New insights from a multi-marker postmortem study

BACKGROUND

Despite significant astrocytic involvement in Parkinson's disease (PD), the knowledge regarding the role of reactive astrogliosis is still at the surface level; largely due to lack of specific biomarkers to track these processes. Novel astroglial PET-tracers BU99008 and Deprenyl, hold immense potential for visualizing reactive astrogliosis in PD.  However, they have not been thoroughly investigated in PD.

METHODS

We employed a multi-marker approach and performed in vitro radioligand binding and autoradiography studies with 3H-BU99008 and 3H-Deprenyl together with astrocytic immunofluorescence and morphometric analyses in the frontal cortex, temporal cortex, caudate and putamen brain regions of PD (n = 4) and control (n = 7) cases.

RESULTS AND DISCUSSION

3H-BU99008 and 3H-Deprenyl showed distinct binding behavior and displayed a diverse array of binding sites (single or multiple) in PD and control brains. Importantly, 3H-BU99008 and 3H-Deprenyl autoradiography studies captured pronounced reactive astrogliosis in PD brain regions, corroborated by marked changes in astrocytic markers, morphology, and cellular processes.

HIGHLIGHTS

Astroglial tracers BU99008 and Deprenyl displayed a range of binding sites with different levels of affinity and proportions (%) in healthy control (CN) and Parkinson's disease (PD) brains. Astroglial tracers BU99008 and Deprenyl showed a highly specific (permanent) high-affinity (HA) binding site in the nanomolar range, which might be consistent across different pathologies. Astroglial tracers BU99008 and Deprenyl highlighted distinct tracer binding behavior, indicating that they might be targeting different subpopulations or specific states of astrocytes in CN and PD brains. Astroglial tracers BU99008 and Deprenyl captured prominent reactive astrogliosis at the advanced/end stages of PD, substantiated by a significant increase in intercellular adhesion molecule 1 (ICAM-1)-positive reactive astrocytes and marked changes in astrocytic morphology and processes.

GPX3 as a Novel and Potential Therapeutic Target in the Shared Molecular Mechanisms of Traumatic Brain Injury and Parkinson's Disease

Background

Traumatic brain injury (TBI) is a prevalent neurological disorder associated with significant public health burdens and long-term risks, including neurodegenerative diseases such as Parkinson's disease (PD). Emerging evidence suggests a strong link between moderate to severe TBI and an elevated risk of PD, though the underlying mechanisms remain poorly understood.

Materials and Methods

Common differentially expressed genes (DEGs) were identified in GEO datasets of patients with traumatic brain injury (TBI) and Parkinson's disease (PD). Further analyses, including GO and KEGG pathway enrichment, protein-protein interaction (PPI) network construction, hub gene identification, as well as miRNA and transcription factor prediction and drug candidate screening, were conducted. Subsequently, the expression of hub genes was validated using additional TBI- and PD-related GEO datasets and the Comparative Toxicogenomics Database (CTD). Finally, the expression of hub genes was further validated in a mouse model of TBI induced by controlled cortical impact (CCI).

Results

Shared transcriptional signatures between TBI and PD were uncovered, highlighting overlapping molecular networks and pathways. The glutathione peroxidase 3 (GPX3) gene emerged as a pivotal hub gene, with its expression significantly altered in both TBI and PD datasets.

Conclusion

This study underscores the critical role of GPX3 in the molecular intersection of TBI and PD, suggesting it as a novel and potential therapeutic target, offering new insights into potential therapeutic strategies.

A perspective of persons with Parkinson's disease on the contribution of alpha-synuclein seed amplification assay biomarker to the diagnosis of Parkinson's disease

Alpha-synuclein is a normal protein, but misfolded forms in the cerebrospinal fluid can be detected using the alpha-synuclein seed amplification assay (αSyn-SAA), a potential biomarker for Parkinson's disease (PD). Some experts consider this assay a 'game changer' for redefining and reclassifying PD. In this article, we, three individuals with PD, share our perspective on the suitability of αSyn-SAA as the basis for a new classification and staging system for PD. We also discuss other biomarkers and their relevance to those with PD, drawing on our research and the scientific background of two authors. We aim to clarify complex media reports and study findings for the PD community. We argue that while αSyn-SAA can identify the presence of pathology, it cannot explain the underlying cause for such pathology or predict the progression of PD. Given the varied biological pathways leading to PD, using αSyn-SAA as a unified biological definition for a new classification system is premature. Further research is needed before it can serve as the foundation for defining and staging Parkinson's disease. Although αSyn-SAA has its place, like the DAT scan, it should be seen as a tool for confirming diagnoses rather than defining them.

Drug utilization study of antiparkinsonian medication in Romania during 25 years

Background

Antiparkinsonian medication has significantly evolved over the last 2 decades, offering various pharmacologic approaches. The aim of this study was to explore the trends and to determine the statistical significance of the observed changes in the antiparkinsonian medication utilization in Romania during 1998-2022.

Methods

This antiparkinsonian drug utilization study used data provided by CEGEDIM Romania, originating from the Pharma and Hospital Report. Quantitative data for each ATC N04 antiparkinsonian medication were converted to total defined daily doses (DDDs) and to DDD/1000inhabitants/day (DDD/TID). The autoregressive integrated moving average (ARIMA) model was employed to determine the statistical significance of the observed changes in the trends of antiparkinsonian drug use.

Results

The utilization of antiparkinsonian medication increased considerably (6-folds) in Romania during the 25 years, from 1.03 DDD/TID in 1998 to 6.22 DDD/TID in 2022. Starting 2005, dopamine precursor (levodopa) became the most used antiparkinsonian drug and remained on this position until the end of the study (13-fold increase from 0.17 in 1998 to 2.30 DDD/TID in 2022). MAO-B inhibitors represented the second most used antiparkinsonian drug class for the majority of the years. Selegiline was the most used until 2017 (0.82 DDD/TID), when a decrease in use was observed and continued until 2022 (0.49 DDD/TID). Utilization of dopamine agonists started in 1999, with less than 0.01 DDD/TID, and increased to 1.47 DDD/TID in 2022. Ropinirole was the most used dopamine agonist (0.56 DDD/TID in 2022). Anticholinergic agents represented the most used antiparkinsonian drugs until 2005. Trihexyphenidyl was the main anticholinergic prescribed with a maximum utilization of 0.82 DDD/TID in 2000 followed by a slight decrease until 2022 (0.56 DDD/TID). Amantadine utilization was mainly constant throughout the 25 years, with 0.32 DDD/TID prescribed in 2022. ARIMA analysis showed that the changes in antiparkinsonian drugs consumption were not statistically significant and overall, the trend for antiparkinsonian drug use demonstrates an upward trajectory.

Conclusion

Antiparkinsonian medication showed an increasing utilization trend in Romania during 1998-2022. Levodopa was the most used antiparkinsonian medicine after 2005, replacing anticholinergic agents. MAO-inhibitors utilization came in second and was followed by dopamine agonists. Observing the trend in antiparkinsonian medication utilization over time is essential for providing insights into their real-world use and uptake in a large population.

m6A Demethylase FTO-Mediated Upregulation of BAP1 Induces Neuronal Ferroptosis via the p53/SLC7A11 Axis in the MPP+/MPTP-Induced Parkinson's Disease Model

Background: Parkinson's disease (PD) is a neurodegenerative disorder characterized by the involvement of ferroptosis in its pathological mechanism. In this study, the effects and mechanism of BRCA1-associated protein 1 (BAP1) on neuronal ferroptosis in PD were evaluated. Methods: A PD mouse model was constructed by injecting mice with MPTP. Nissl staining, immunohistochemistry, immunofluorescence, and Prussian blue staining evaluated histopathology and iron distribution. The PD cell model was constructed by subjecting SK-N-SH cells to MPP+. The m6A level of BAP1 was assessed by MeRIP. mRNA levels of BAP1, FTO, IGF2BP1, METTL3, YTHDF2, and SLC7A11 were evaluated utilizing RT-qPCR. Protein levels of BAP1, FTO, IGF2BP1, METTL3, YTHDF2, SLC7A11, and p53 were measured by Western blot. Cell viability was assessed using CCK-8 assay, and TUNEL was used for assessing apoptosis. The levels of MDA, GSH, SOD, and Fe2+ were also measured. The interactions among molecules were verified using RIP assay, dual luciferase reporter assay, and ChIP assay. Results: SK-N-SH cells treated with MPP+ showed a decrease in overall m6A levels of BAP1. FTO facilitated m6A demethylation of BAP1, leading to an increased level of expression of BAP1. m6A-binding protein, YTHDF2 recognized and decayed methylated mRNA of BAP1, leading to the reduced BAP1 stability. The FTO/BAP1 axis promoted MPP+-induced ferroptosis by suppressing SLC7A11. BAP1, in collaboration with p53, reduced the level of expression of SLC7A11. Knocking down BAP1 mitigated ferroptosis in an MPTP mouse model. Conclusion: m6A-mediated modification of BAP1 regulates neuronal ferroptosis by cooperating with p53 to decrease the level of SLC7A11. Thus, BAP1 may be a potential therapeutic target for PD treatment.

Genetic analyses identify brain functional networks associated with the risk of Parkinson's disease and drug-induced parkinsonism

Brain functional networks are associated with parkinsonism in observational studies. However, the causal effects between brain functional networks and parkinsonism remain unclear. We aimed to assess the potential bidirectional causal associations between 191 brain resting-state functional magnetic resonance imaging (rsfMRI) phenotypes and parkinsonism including Parkinson's disease (PD) and drug-induced parkinsonism (DIP). We used Mendelian randomization (MR) to assess the bidirectional associations between brain rsfMRI phenotypes and parkinsonism, followed by several sensitivity analyses for robustness validation. In the forward MR analyses, we found that three rsfMRI phenotypes genetically determined the risk of parkinsonism. The connectivity in the visual network decreased the risk of PD (OR = 0.391, 95% CI = 0.235 ~ 0.649, P = 2.83 × 10-4, P_FDR = 0.039). The connectivity of salience and motor networks increased the risk of DIP (OR = 4.102, 95% CI = 1.903 ~ 8.845, P = 3.17 × 10-4, P_FDR = 0.044). The connectivity of limbic and default mode networks increased the risk of DIP (OR = 14.526, 95% CI = 3.130 ~ 67.408, P = 6.32 × 10-4, P_FDR = 0.0437). The reverse MR analysis indicated that PD and DIP had no effect on brain rsfMRI phenotypes. Our findings reveal causal relationships between brain functional networks and parkinsonism, providing important interventional and therapeutic targets for different parkinsonism.

Sex and gender differences in the molecular etiology of Parkinson's disease: considerations for study design and data analysis

Parkinson's disease (PD) is more prevalent in men than women, and presents with different clinical features in each sex. Despite widespread recognition of these differences, females are under-represented in clinical and experimental studies of PD, and much remains to be elucidated regarding the biological underpinnings of sex differences in PD. In this review, we summarize known contributors to sex differences in PD etiology across the life course, with a focus on neurological development and gene regulation. Sex differences that are established at conception and heightened during adolescence and midlife may partially embed future PD risk, due to the complex interactions between gonadal hormones, gene regulation, lifestyle factors, and aging. While the neuroprotective properties of estrogen are strongly implicated in reduced prevalence of PD in women, interactions with genotype and gender-biased lifestyle factors are incompletely understood. Consideration of sex and gender-related factors in study design, data analysis, and interpretation have the power to expedite our knowledge of the etiology of PD in men and in women, and to inform prevention and therapeutic strategies tailored to each sex.

Epidemiologic Risk and Prevention and Interventions in Parkinson Disease: From a Nutrition-Based Perspective

Parkinson disease (PD) is a prevalent neurodegenerative disorder associated with aging. Current treatments for PD primarily focus on alleviating symptoms rather than altering the progression of the disease. The sporadic form of PD, which accounts for most cases, is thought to arise from a complex interaction between genetic predispositions and environmental factors. This review aimed to examine epidemiologic evidence regarding nutrition-related exposure factors and their associations with risk of developing PD. We proposed a tentative conclusion for each factor based on the available evidence. These associations may vary by gender and depend on dietary intake patterns and adherence. We also reviewed clinical trials on nutrition-related interventions for PD symptoms and progression. Future clinical trials may benefit from combining nutrition factors in intervention and testing within single-gender cohorts or subgroups defined by epidemiologic outcomes.

Palmatine Ameliorates Motor Deficits and Dopaminergic Neuron Loss by Regulating NLRP3 Inflammasome through Mitophagy in Parkinson's Disease Model Mice

NLRP3 inflammasomes-mediated proinflammatory response and mitochondrial dysfunction play a critical role in the etiology and pathogenesis of Parkinson's disease. Negative regulation of NLRP3 inflammasome activation through mitophagy may be an important strategy to control NLRP3 inflammasome-mediated proinflammatory responses. Palmatine (PAL), an isoquinoline alkaloid found in various of plants, has potent pharmacological effects such as anti-inflammatory and anti-oxidation. However, the specific role of PAL in the pathology of Parkinson's disease remains unclear. In this study, we found that treatment with PAL improved motor deficits and reduced the loss of dopaminergic neurons in MPTP mice. Further results showed that PAL promoted mitophagy and inhibited the proinflammatory response mediated by NLRP3 inflammasomes. In addition, chloroquine (CQ, mitophagy inhibitor) attenuated the ameliorative effects of PAL on the motor deficits and dopaminergic neuron damage, as well as the inhibitory effect of PAL on NLRP3 inflammasome. Collectively, these results provide strong evidence that PAL ameliorates motor deficits and dopaminergic neuron death in Parkinson's disease, and the mechanism may be related to its inhibition of NLRP3 inflammasome activation via promoting mitophagy.

Ergothioneine exerts neuroprotective effects in Parkinson's disease: Targeting α-synuclein aggregation and oxidative stress

Ergothioneine (EGT) is a natural dietary antioxidant derived from certain edible mushrooms, commonly used as a food additive and supplement, but its effects on Parkinson's Disease (PD) are still unclear. The accumulation of α-synuclein (α-syn) plays a pivotal role in the pathogenesis and development of PD. Here, this study demonstrated that EGT effectively inhibits α-syn aggregation, disrupts mature fibers, and reduces associated cytotoxicity and oxidative stress. The beneficial effects of EGT were confirmed in Caenorhabditis elegans, where it protected dopaminergic neurons, prolonged lifespan and enhanced behavioral functions by reducing α-syn plaque accumulation and associated oxidative stress. Molecular dynamics simulation revealed that EGT interacts directly with α-syn pentamer through van der Waals and electrostatic forces, disrupting the structural stability of the preformed pentamer. Furthermore, animal studies validated that EGT alleviated neuronal damage and improved behavioral deficits by reducing α-syn aggregation, oxidative stress and inflammatory response. In conclusion, EGT presents promising potential as a dietary supplement for preventing and alleviating PD.

Association between life's essential 8 and Parkinson's disease: a case-control study

OBJECTIVES

Life's essential 8 (LE8) is an emerging approach for accessing and quantifying cardiovascular health (CVH), but the effect on Parkinson's disease (PD) is still unclear. This study aimed to elucidate the association between LE8 metrics and PD in the US adults.

METHODS

Data of 26,975 participants were extracted from the last 7 National Health and Nutrition Examination Survey (NHANES) cycles (2005-2018). The LE8 metrics were calculated according to the American Heart Association criterion, and participants were divided into 3 groups using tertile range. Multivariate logistic regression models were constructed to explore the association between LE8 metrics and PD. Sensitivity analysis was conducted to verify robustness. A nonlinear linkage was evaluated via restricted cubic spline (RCS). The stability of this effect was validated by subgroup analysis and interaction test.

RESULTS

A total of 26,975 eligible participants (including 271 PD cases and 26,704 non-PD cases) were included in this study. The multivariate logistic regression models revealed a reverse association of continuous LE8 metrics with PD with ORs of 0.97 (unadjusted model [95% CI: 0.96-0.98, P < 0.01], partially adjusted model [95% CI: 0.97-0.98, P < 0.01], fully adjusted model [95% CI: 0.95-0.98, P < 0.01]). Compared to those of low group, the ORs for high group were 0.37 (95% CI: 0.27-0.50, P < 0.01) in unadjusted model, 0.51 (95% CI: 0.36-0.72, P < 0.01) in partially adjusted model, and 0.51 (95% CI: 0.32-0.81, P < 0.01) in fully adjusted model. The sensitivity analysis ensured the robustness of the observed LE8-PD association. A nonlinear relationship (P nonlinearity < 0.01) was observed via RCS analysis. The subgroup analysis showed that participants'gender might impact the strength of LE8 metrics-PD association (P interaction = 0.029).

CONCLUSIONS

CVH, as delineated by LE8 metrics, was reversely associated with PD in the dose-response pattern, more pronounced in female compared to male. These findings highlight the potential of the LE8 metrics to guide targeted strategies for addressing gender-based CVH disparities, offering beneficial insights for the tertiary prevention of PD.

Individualized brain radiomics-based network tracks distinct subtypes and abnormal patterns in prodromal Parkinson's disease

Individuals in the prodromal phase of Parkinson's disease (PD) exhibit significant heterogeneity and can be divided into distinct subtypes based on clinical symptoms, pathological mechanisms, and brain network patterns. However, little has been done regarding the valid subtyping of prodromal PD, which hinders the early diagnosis of PD. Therefore, we aimed to identify the subtypes of prodromal PD using the brain radiomics-based network and examine the unique patterns linked to the clinical presentations of each subtype. Individualized brain radiomics-based network was constructed for normal controls (NC; N = 110), prodromal PD patients (N = 262), and PD patients (N = 108). A data-driven clustering approach using the radiomics-based network was carried out to cluster prodromal PD patients into higher-/lower-risk subtypes. Then, the dissociated patterns of clinical manifestations, anatomical structure alterations, and gene expression between these two subtypes were evaluated. Clustering findings indicated that one prodromal PD subtype closely resembled the pattern of NCs (N-P; N = 159), while the other was similar to the pattern of PD (P-P; N = 103). Significant differences were observed between the subtypes in terms of multiple clinical measurements, neuroimaging for morphological changes, and gene enrichment for synaptic transmission. Identification of prodromal PD subtypes based on brain connectomes and a full understanding of heterogeneity at this phase could inform early and accurate PD diagnosis and effective neuroprotective interventions.

Validation of Parkinson's Disease Ascertainment in the Veterans Administration Electronic Medical Record

BACKGROUND

Electronic medical record (EMR)-based studies hold great potential for epidemiologic investigations of Parkinson's disease (PD) causal factors and phenomenology, but diagnostic misclassification may obscure or bias inferences.

OBJECTIVES

The aims were to determine the validity of PD diagnostic codes in the Veterans Administration (VA) national electronic medical databases and develop recommendations for maximizing ascertainment accuracy.

METHODS

We investigated a cohort of 146,776 veterans who utilized VA healthcare between 1999 and 2021. We reviewed the medical records of individuals with a PD International Classification of Diseases (ICD) code in outpatient, inpatient, or community care encounters to assign a gold-standard diagnosis. We determined diagnostic accuracy based on provider type, coding frequency, medications, and potentially exclusionary ICD codes overall and by race.

RESULTS

A total of 377 of 810 (46.5%) with a PD ICD code had PD. Veterans whose PD was coded by a PD-specialist neurologist were most likely to have PD (83.6%), but sensitivity was low (15.0%). Diagnostic accuracy decreased for PD coded by any neurologist (66.9%), but sensitivity improved (69.4%). Requiring two or more PD codes in combination with two or more levodopa prescriptions improved accuracy, particularly among nonneurologists. Neuroleptic-induced parkinsonism was the most frequent diagnosis in those without PD (15.6%). Accuracy was lower in Black (29.0%) than White (50.5%) veterans regardless of provider type (miscoding odds ratio 2.5, 95% confidence interval 1.7-3.6).

CONCLUSIONS

These results highlight the limitations of EMR-based PD ascertainment. Researchers can maximize accuracy by considering provider specialty, coding frequency, pharmacy data, and exclusionary diagnoses, but some degree of record review is required to ensure high accuracy. Higher miscoding among Black veterans warrants further study. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

The role of mitochondrial remodeling in neurodegenerative diseases

Neurodegenerative diseases are a group of diseases that pose a serious threat to human health, such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD) and Amyotrophic Lateral Sclerosis (ALS). In recent years, it has been found that mitochondrial remodeling plays an important role in the onset and progression of neurodegenerative diseases. Mitochondrial remodeling refers to the dynamic regulatory process of mitochondrial morphology, number and function, which can affect neuronal cell function and survival by regulating mechanisms such as mitochondrial fusion, division, clearance and biosynthesis. Mitochondrial dysfunction is an important intrinsic cause of the pathogenesis of neurodegenerative diseases. Mitochondrial remodeling abnormalities are involved in energy metabolism in neurodegenerative diseases. Pathological changes in mitochondrial function and morphology, as well as interactions with other organelles, can affect the energy metabolism of dopaminergic neurons and participate in the development of neurodegenerative diseases. Since the number of patients with PD and AD has been increasing year by year in recent years, it is extremely important to take effective interventions to significantly reduce the number of morbidities and to improve people's quality of life. More and more researchers have suggested that mitochondrial remodeling and related dynamics may positively affect neurodegenerative diseases in terms of neuronal and self-adaptation to the surrounding environment. Mitochondrial remodeling mainly involves its own fission and fusion, energy metabolism, changes in channels, mitophagy, and interactions with other cellular organelles. This review will provide a systematic summary of the role of mitochondrial remodeling in neurodegenerative diseases, with the aim of providing new ideas and strategies for further research on the treatment of neurodegenerative diseases.

The prospective therapeutic benefits of sesamol: neuroprotection in neurological diseases

Oxidative stress is recognized as a critical contributor to the advancement of neurological diseases, thereby rendering the alleviation of oxidative stress a pivotal strategy in the therapeutic management of such conditions. Sesamol, the principal constituent of sesame oil, has been the subject of extensive research due to its significant antioxidant properties, especially its ability to effectively counteract oxidative stress within the central nervous system and confer neuroprotection. While sesamol demonstrates potential in the treatment and prevention of neurological diseases, its modulation of oxidative stress is complex and not yet fully understood. This review delves into the neuroprotective effects arising from sesamol's antioxidant properties, analyzing how its antioxidative capabilities impact neurological diseases. It provides a theoretical foundation and unveils potential novel therapeutic applications of sesamol in the treatment of neurological disorders through the modulation of oxidative stress.

Co-occurrence of Parkinson's disease and Retinitis Pigmentosa: A genetic and in silico analysis

INTRODUCTION

Parkinson's disease (PD) is primarily driven by the protein Alpha Synuclein (A-Syn) accumulation. Synphilin-1 protein, encoded by the SNCAIP gene, which co-localizes with A-Syn is a known risk factor for PD. Retinitis pigmentosa (RP), is a cluster of retinal degenerative disorders, and Cyclic Nucleotide Gated channel subunit Alpha 1 (CNGA1) is one of the initial genes associated with RP. Patients with PD can have various kinds of visual dysfunction as a non-motor manifestation, but to date, CNGA1 mutation and RP as a PD associated visual symptom has not been reported. We report a mutation in the SNCAIP gene in a PD patient, not reported earlier, and its co-occurrence with RP-associated CNGA1 gene mutation.

METHOD

Whole exome sequencing (WES) of the patient DNA sample and in-silico protein-protein interaction (PPI) analysis performed to find out proteins interacting with SNCAIP relevant concerning reported mutation of SNCAIP and further, CNGA1 interaction with SNCAIP.

RESULT

We are reporting, a missense mutation (p.Thr64Ser) at the SNCAIP gene, co-occurring with a missense variation (p.Gly509Arg) in the CNGA1 gene. In silico PPI analysis suggests SIAH1 as an important protein affected by SNCAIP mutation. LGALS4 and SNCA (gene encoding A-Syn) are common interactors between SNCAIP and CNGA1.

CONCLUSION

The current study has determined the co-occurrence of RP and PD, whole exome sequencing ascertains the mutations in SNCAIP and CNGA1 genes, which could be the cause of PD and RP co-occurrence.

Present insights into the progress in gene therapy delivery systems for central nervous system diseases

Central nervous system (CNS) diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), spinal cord injury (SCI), and ischemic strokes and certain rare diseases, such as amyotrophic lateral sclerosis (ALS) and ataxia, present significant obstacles to treatment using conventional molecular pharmaceuticals. Gene therapy, with its ability to target previously "undruggable" proteins with high specificity and safety, is increasingly utilized in both preclinical and clinical research for CNS ailments. As our comprehension of the pathophysiology of these conditions deepens, gene therapy stands out as a versatile and promising strategy with the potential to both prevent and treat these diseases. Despite the remarkable progress in refining and enhancing the structural design of gene therapy agents, substantial obstacles persist in their effective and safe delivery within living systems. To surmount these obstacles, a diverse array of gene delivery systems has been devised and continuously improved. Notably, Adeno-Associated Virus (AAVs)-based viral gene vectors and lipid-based nanocarriers have each advanced the in vivo delivery of gene therapies to various extents. This review aims to concisely summarize the pathophysiological foundations of CNS diseases and to shed light on the latest advancements in gene delivery vector technologies. It discusses the primary categories of these vectors, their respective advantages and limitations, and their specialized uses in the context of gene therapy delivery.

A novel feature extraction method based on dynamic handwriting for Parkinson's disease detection

Parkinson's disease (PD) is a common disease of the elderly. Given the easy accessibility of handwriting samples, many researchers have proposed handwriting-based detection methods for Parkinson's disease. Extracting more discriminative features from handwriting is an important step. Although many features have been proposed in previous researches, the insight analysis of the combination of handwriting's kinematic, pressure, and angle dynamic features is lacking. Moreover, most existing feature is incompletely represented, with feature information lost. Therefore, to solve the above problems, a new feature extraction approach for PD detection is proposed using handwriting. First, built on the kinematic, pressure, and angle dynamic features, we propose a moment feature by composed these three types of features, an overall representation of these three types of features information. Then, we proposed a feature extraction method to extract time-frequency-based statistical (TF-ST) features from dynamic handwriting features in terms of their temporal and frequency characteristics. Finally, we proposed an escape Coati Optimization Algorithm (eCOA) for global optimization to enhance classification performance. Self-constructed and public datasets are used to verify the proposed method's effectiveness respectively. The experimental results showed an accuracy of 97.95% and 98.67%, a sensitivity of 98.15% (average) and 97.78%, a specificity of 99.17% (average) and 100%, and an AUC of 98.66% (average) and 98.89%. The code is available at https://github.com/dreamhcy/MLforPD.

MicroRNAs in Parkinson's disease: From pathogenesis to diagnostics and therapeutic strategies

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by pathological changes, including the loss of dopaminergic neurons and abnormal aggregation of α-synuclein (α-syn). Certain cellular and molecular events are involved; however, the origin and significance of these events remain uncertain. The discovery of microRNAs (miRNAs) predicted to play a pivotal role in various regulatory processes has emerged. Studies on the dysregulation of miRNAs in PD pathogenesis, diagnosis, and treatment have recently gained attention. This review aims to encapsulate recent research developments concerning the function of miRNAs in the pathophysiology of PD and their prospective applications as diagnostic and therapeutic biomarkers, targets, and pharmaceuticals. The most effective drug delivery approach for the treatment of PD, transnasal-cerebral drug delivery, has also been briefly described. The advantage of this delivery strategy is its capacity to bypass the blood-brain barrier, enabling direct administration of medication to the brain, which improves therapeutic efficacy and minimizes side effects.

Efficient recognition of Parkinson's disease mice on stepping characters with CNN

Parkinson's disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts the quality of life for over 12 million patients. This study aims to enhance the accuracy of early diagnosis of PD through non-invasive methods, with the goal of enabling earlier intervention in the disease process. To this end, we constructed an open-field environment using flexible sensors under dark conditions, conducting experiments on a mouse model of Parkinson's disease alongside a normal control group. By processing footprint images collected in the absence of light-employing numerical area summation for noise reduction, adaptive enhancement algorithms based on pixel values, and a high-accuracy Convolutional Neural Network algorithm. And integrating motion data analysis, we achieved effective fusion of footprint images and behavioral data. After comprehensive analysis using decision-level fusion techniques and a Naive Bayes classifier, the results showed that the average classification accuracy for PD mice reached 96.56% when employing a multimodal data fusion strategy, demonstrating a significant advantage over using a single image recognition technique. This research not only highlights the substantial potential of applying multimodal data fusion strategies in the early detection of Parkinson's disease but also proves that such an approach can significantly improve diagnostic accuracy. The findings provide new insights into the early diagnosis of Parkinson's disease and other neurodegenerative disorders.

Cannabis oil in treating Parkinson's disease: improvement of motor and non-motor symptoms: a case report

Parkinson's disease (PD) is characterized by progressive loss of dopaminergic neurons in the substantia nigra pars compacta, which leads to a reduction in the production of dopamine. Medication with levodopa becomes less effective as the disease progresses. Despite the excellent results observed in clinical practice with the medicinal use of Cannabis in the treatment of PD, the level of scientific evidence is still limited due to the small number of studies published in this field. We present the case of a 77-year-old man diagnosed 22 years ago with PD in an advanced stage, with significant bradykinesia, tremor, and rigidity along with the inability to maintain an upright position and walk, exacerbated by a femur fracture. He also had advanced dysphagia, resulting in a gastrostomy. Although lucid, he showed no interest in conversation and tended to become depressed and isolated. He used Prolopa® with no satisfactory therapeutic response. After starting treatment with Cannabis sativa oil, he is now able to walk around the house frequently and eat pasty food regularly without choking or broncho-aspiration episodes. There has also been a significant improvement in non-motor symptoms; he is more active, cheerful, communicative, and attentive to his surroundings. Further studies are needed to elucidate these results and the mechanisms of action of cannabinoids through which they exert possible neuroprotective and neuroreparative effects. These compelling results suggest that cannabis oil may offer a valuable and effective therapeutic option for individuals with Parkinson's disease.

Abnormal dynamic features of cortical microstates for detecting early-stage Parkinson's disease by resting-state electroencephalography: Systematic analysis of the influence of eye condition

Resting state electroencephalography (EEG) has proved useful in studying electrophysiological changes in neurodegenerative diseases. In many neuropathologies, microstate analysis of the eyes-closed (EC) scalp EEG is a robust and highly reproducible technique for assessing topological changes with high temporal resolution. However, scalp EEG microstate maps tend to underestimate the non-occipital or non-alpha-band networks, which can also be used to detect neuropathological changes. Recent evidence has shown that the source-space microstates can characterize distinct functional connectivity patterns but its clinical ability to detect neuropathological changes has not been demonstrated so far. It should also be remembered that the eye condition may play an important role in neural activity dynamics. The aim of this study was to systematically characterize the dynamic neuropathological features of sensor-space and source-space EEG microstates in PD patients with no cognitive impairment in both EC and EO conditions with the aim of identifying potential biomarkers that could be used as a complementary clinical screening method for early PD detection. We found that the dynamic features of the source-space microstates were more sensitive in detecting PD than the sensor-space microstates, while EO was able to detect neuropathological changes in PD patients better than EC. In EO, PD disease exhibited significantly higher occurrence and coverage in visual-network related source-space microstates and abnormally high duration in sensorimotor network-related microstates. Our results suggest that the source-space microstate analysis of resting-state EEG could provide robust biomarkers to detect early-stage PD, which would allow the development of patient-oriented strategies to prevent the disease and improve the patients' quality of life.

Electrochemical biosensors in early detection of Parkinson disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the motor system, with symptoms including tremors, rigidity, bradykinesia, and postural instability. Affecting over six million people globally, PD's pathophysiology is marked by the loss of dopaminergic neurons in the substantia nigra. Early diagnosis is crucial for effective management, yet current methods are limited by low sensitivity, high cost, and the need for advanced equipment. Electrochemical biosensors have emerged as promising tools for early PD diagnosis, converting biological reactions into measurable electrical signals for evaluating PD biomarkers. Advances in nanotechnology and material science have led to innovative sensing platforms with enhanced sensitivity and selectivity. Key biomarkers such as alpha-synuclein (α-syn), dopamine (DA), and microRNAs (miRNAs) have been targeted using these biosensors. For instance, gold nanoparticle-modified graphene immunosensors have shown ultra-sensitive detection of α-syn, while graphene-based biosensors have demonstrated high sensitivity for DA detection. Additionally, nanobiosensors for miR-195 and electrochemical aptasensors have shown potential for early PD diagnosis. The integration of nanomaterials like gold nanoparticles, quantum dots, and carbon nanotubes has further advanced the field, enhancing electrochemical activity and sensitivity. These developments offer a reliable, rapid, and cost-effective approach for early PD diagnosis, paving the way for better management and treatment. Continued research is essential for the commercialization and clinical integration of these biosensors, ultimately improving patient outcomes.

Filipinos and Parkinson's disease: A scoping review of the literature

BACKGROUND

Filipinos are the third largest sub-group of Asian Americans in the United States and have greater socioeconomic and health disparities than many other Asian sub-groups [1,2,3,4,5,6,7,8]. Characteristics of Filipino patients with Parkinson's disease (PD) have not been adequately studied.

OBJECTIVE

To scope the extent, range and nature of current knowledge on PD in Filipino-American (FA) patients in contrast to Filipino patients in general.

METHODS

We undertook a scoping review including all types of research relating to PD in Filipinos. We searched the following electronic databases: MEDLINE via PubMed, Google Scholar, Cochrane Library, Herdin and Journals@OVID (searched 1944-November 2024). Criteria for inclusion were peer-reviewed empirical articles published in English that specifically included and described Filipino patients with PD. We reviewed all eligible articles and categorized them by general topical themes.

RESULTS

The scoping review resulted in 48 eligible papers (Fig. 1). There were 7 papers that discussed PD in FA, and 35 papers that discussed PD in the Philippines. The main themes identified from the scoping review were: epidemiology/etiology (n = 19, 39 %), clinical manifestations and management (n = 20, 42 %), and access to PD care (n = 9, 19 %).

CONCLUSION

There is a paucity of information on PD in the FA population, with the majority of studies from the Philippines. Further studies on FA in the US, when compared to the studies from the Philippines, may shed light on how differences in the environment and sociodemographic factors may influence the nature and course of PD.

Amyotrophic Lateral Sclerosis and Parkinson's Disease: Brain Tissue Transcriptome Analysis Reveals Interactions

This study utilises amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) human brain samples from the GEO database and employs differential expression gene (DEG) analysis to identify genes that are pivotal in both neurodegenerative diseases. Through in depth GO and KEGG enrichment analyses, we elucidated the biological functions and potential pathways associated with these DEGs. Furthermore, by constructing protein‒protein interaction networks, we highlight the significance of shared DEGs in both cellular physiology and disease contexts. Analysis of drug‒gene associations revealed potential therapeutic compounds linked to ALS and PD treatment. Additionally, we explored the interactions between transcription factors, miRNAs, and common DEGs, revealing aspects of gene regulatory networks. This study provides insights into the molecular mechanisms of ALS and PD, offering valuable contributions to ongoing research and potential therapeutic avenues.

Motor Assessment Timed Test (MATT): A New Timed Test to Assess Functional Mobility in Parkinson's Disease Patients

Background/Objectives: Parkinson's disease (PD) is a neurodegenerative disorder that significantly impairs motor function, leading to mobility challenges and an increased risk of falls. Current assessment tools often inadequately measure the complexities of motor impairments associated with PD, highlighting the need for a reliable tool. This study introduces the Motor Assessment Timed Test (MATT), designed to assess functional mobility in PD patients. Methods: A cross-sectional study was conducted involving 57 participants (38 men and 19 women) aged 44 to 84, diagnosed with idiopathic PD. Participants were recruited from three PD associations and underwent a series of assessments, including MATT, to evaluate gait, balance, and dual-task performance under conditions that reflect real-life challenges faced by individuals with PD. Results: MATT demonstrated excellent reliability with intra-rater reliability (ICC = 0.99), inter-rater reliability (ICC = 0.96-0.99), and test-retest reliability (ICC = 0.93-0.99). The coefficient of variation for total time and each segment ranged from 4.73% to 13.71%, indicating consistent performance across trials. The concurrent validity showed very high correlations with established tools such as the Timed Up and Go (TUG) test (ρ = 0.86, p < 0.001) and the Berg Balance Scale (BBS) (ρ = -0.83, p < 0.001), among others. Only 7.3% of participants reported difficulties in understanding the MATT, predominantly those in advanced stages of the disease. In addition, 23.6% of participants experienced significant challenges in performing the test, particularly individuals with lower Mini-Mental State Examination (MMSE) scores (≤ 26) and more advanced disease progression. Conclusions: MATT is a promising tool for assessing motor complications in PD, offering a comprehensive evaluation of functional mobility. Its implementation in clinical practice could enhance the management of PD, facilitating tailored interventions and improving patient outcomes.

Causal association between cerebrospinal fluid metabolites and Parkinson's disease: A two-sample bidirectional mendelian randomization study

OBJECTIVE

Observational studies suggest CSF metabolites may be linked to Parkinson's disease (PD) onset, but causality is uncertain. This study uses a two-sample bidirectional Mendelian randomization approach to investigate the causal relationship between CSF metabolites and PD.

METHODS

Data on 338 CSF metabolites and PD-related traits were obtained from genome-wide association studies (GWAS). Causal relationships between CSF metabolites and PD were assessed using inverse variance-weighted (IVW), MR-Egger regression, weighted median method, simple mode, and weighted mode. Sensitivity analyses for heterogeneity and pleiotropy were conducted to explore the robustness of the results.

RESULTS

Our analysis identified an association between nine CSF metabolites and PD. Notably, significant increases in the risk of PD were observed for Ribitol (IVW, OR: 1.45; 95 % CI: 1.09-1.91; P = 9.04 ×E-03), Lysine (IVW, OR: 1.54; 95 % CI: 1.09-2.17; P = 1.24 ×E-02), and O-sulfo-l-tyrosine (IVW, OR: 1.38; 95 % CI: 1.06-1.79; P = 1.60 ×E-02). Additionally, we found that elevated levels of oxidized cysteinyl-glycine and 1,5-anhydroglucitol were associated with a decreased risk of PD. Furthermore, PD was associated with alterations in 12 CSF metabolites, including significant increases in Acetoacetate (IVW, OR: 1.15; 95 % CI: 1.02-1.30; P = 1.79 ×E-02), S-methylcysteine (IVW, OR: 1.14; 95 % CI: 1.02-1.29; P = 2.62 ×E-02), and N-acetyl-3-methylhistidine (IVW, OR: 1.12; 95 % CI: 1.01-1.23; P = 2.22 ×E-02).

CONCLUSION

The identified CSF metabolites may serve as potential CSF metabolic biomarkers for screening and preventing PD in clinical practice and could also be considered as candidate molecules for future mechanistic exploration and drug target selection.

Advancing Parkinson's diagnosis: seed amplification assay for α-synuclein detection in minimally invasive samples

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by tremor, rigidity, and bradykinesia, beginning with early loss of dopaminergic neurons in the ventrolateral substantia nigra and advancing to broader neurodegeneration in the midbrain. The clinical heterogeneity of PD and the lack of specific diagnostic tests present significant challenges, highlighting the need for reliable biomarkers for early diagnosis. Alpha-synuclein (α-Syn), a protein aggregating into Lewy bodies and neurites in PD patients, has emerged as a key biomarker due to its central role in PD pathophysiology and potential to reflect pathological processes. Additionally, α-Syn allows earlier differentiation between PD and other neurodegenerative disorders with similar symptoms. Currently, detection of α-Syn pathology in post-mortem brain tissue remains the primary means of achieving a conclusive diagnosis, often revealing significant misdiagnoses. Seed amplification assay (SAA), initially developed for prion diseases, has been adapted to detect α-Syn aggregates in cerebrospinal fluid, showing promise for early diagnosis. Recent studies have demonstrated that SAA can also detect α-Syn aggregates in peripheral samples collected via minimally invasive procedures, such as skin, olfactory mucosa, saliva, and blood. However, the lack of standardized protocols limits clinical application. Standardizing protocols is essential to improve assay reliability and enable accurate patient identification for emerging therapies. This review examines studies on SAA for detecting α-Syn aggregates in minimally invasive samples, focusing on sample collection, processing, and reaction conditions.

Association between deep gray matter iron deposition and clinical symptoms in Parkinson's disease: a quantitative susceptibility mapping study

Purpose

This study aimed to assess the association between motor and non-motor symptoms of Parkinson's disease (PD) and iron accumulation within the deep gray matter of the brain by Quantitative Susceptibility Mapping (QSM).

Methods

Fifty-six PD patients and twenty-nine healthy controls were recruited in this study. According to the Hoehn and Yahr (H-Y) stage score, PD patients were divided into early stage (H-Y ≤ 2) and advanced stage (H-Y > 2) groups. Specifically, the Regions of Interest (ROIs) encompassed the substantia nigra (SN), red nucleus (RN), caudate nucleus (CN), globus pallidus (GP) and putamen (PT). Meanwhile, various rating scales were used to assess the clinical symptoms of PD.

Results

Compared to healthy controls (HCs), PD patients showed a significant increase in magnetic susceptibility values (MSVs) within the SN and GP. Further comparisons indicated that the MSVs of the SN, PT, GP and CN are all higher in advanced stages than in early stages. Significant positive correlations were observed between the MSVs of the SN and scores on the UPDRS-III, HAMA, and HAMD (r = 0.310, p = 0.020; r = 0.273, p = 0.042; r = 0.342, p = 0.010, respectively). Likewise, the MSVs of the GP demonstrated notable correlations with HAMA and HAMD scores (r = 0.275, p = 0.040; r = 0.415, p = 0.001). Additionally, a significant correlation was found between the MSVs of the PT and HAMD scores (r = 0.360, p = 0.006). Furthermore, we identified a significant negative correlation between MMSE scores and the MSVs of both the PT and GP (r = -0.268, p = 0.046; r = -0.305, p = 0.022).

Conclusion

Our study revealed that QSM possesses the capability to serve as a biomarker for PD. Significant correlations were found between clinical features and the iron deposition in the nigrostriatal system.

A methodological scoping review on EMG processing and synergy-based results in muscle synergy studies in Parkinson's disease

Introduction

Parkinson's Disease is the second most common neurodegenerative disease in the world. It affects mainly people over 65 and the incidence increases with age. It is characterized by motor and non-motor symptoms and several clinical manifestations. The most evident symptom that affects all patients with Parkinson's Disease is the impairment of motor control, including bradykinesia, tremor, joint rigidity, and postural instability. In the literature, it has been evaluated with muscle synergies, a well-known method for evaluating motor control at the muscular level. However, few studies are available and there is still a major gap to fill to exploit the potential of the method for assessing motor control in Parkinson's Disease, both in the understanding of physiopathology and clinical practice.

Methods

In the light of understanding and fostering future developments for the field, in this review we initially screened 212 papers on Scopus and Web of Science and selected 15 of them to summarize the main features of investigations that employed muscle synergies to analyze patients with Parkinson's Disease. We detailed the features of the screened papers by reporting the clinical findings, a detailed report of EMG processing choices and synergy-based results.

Results

We found that synergistic control is in general altered in patients with Parkinson's Disease, but it can improve if patients are subjected to pharmacological and rehabilitation therapies. Moreover, a further understanding of synergistic control in Parkinson's patients is needed.

Discussion

We discuss the future developments in the field with a detailed assessment of the topic on the view of physicians, including the most promising lines of research for clinical practice and from the perspective of engineers, for methodological application of synergistic approaches.

Effects of long-term Tai Chi vs. aerobic exercise on antioxidant activity and cognitive function in individuals with Parkinson's disease

An imbalance between the generation of reactive oxygen species and the body's antioxidant defense mechanisms is closely related to the development and progression of Parkinson's disease (PD). Considering that physical exercise is a potential therapeutic intervention for modulating oxidative stress markers and cognitive function in PD, the primary purpose of this study was to compare the effects of different long-term exercise modalities on antioxidants and cognitive performance in patients with PD. In addition, the secondary purpose was to explore whether changes in the levels of these biochemical markers are associated with alterations in cognitive performance pre- and post-intervention. In all, 61 participants were randomly divided into the aerobic exercise (AE, n=20), Tai Chi exercise (TCE, n=21), or control (n=20) group. Blood samples were collected before and after a 12-week intervention period for the analysis of antioxidant markers [leukocyte 8-hydroxydeoxyguanosine (8-OHdG), catalase (CAT), glutathione (GSH), glutathione peroxidase (GSH-Px), oxidized glutathione (GSSG), superoxide dismutase (SOD), and uric acid (UA)]. Cognitive function was evaluated using the Mini-Mental State Examination (MMSE). Although no significant changes were observed in the activity of 8-OhdG, GSH-Px, GSSG, GSH:GSSG ratio, SOD, and cognitive performance in the AE and TCE groups, the 12-week AE intervention led to a significant increase in CAT and GSH levels, along with a significantly decrease in UA levels among individuals with PD. Conversely, the TCE intervention resulted in a significant increase in GSH levels. However, SOD activity and MMSE scores were significantly decreased after 12 weeks in the control group. The correlations between changes in MMSE scores and changes in the levels of GSH and UA prior to and after the intervention reached significance in the AE group. Thus, long-term AE and TCE might serve as effective strategies for reducing oxidative damage and preserving cognitive function in PD, with AE exhibiting greater benefits compared with TCE. These findings hold potential clinical relevance as complementary measures to standard medical treatments and alternative therapies, such as antioxidant supplements and dietary adjustments, particularly for individuals in the early stages of PD.

MRI evaluation of neuroprotective effects of Astragaloside Ⅳ on rotenone-induced late-stage Parkinson's disease mice

Astragaloside Ⅳ (AS-Ⅳ) improved the motor behavior of Parkinson's disease (PD) mouse but the alteration of imaging in the PD mice brain was unclear. PD models were established by unilateral injection of rotenone (ROT) into the substantia nigra pars compacta (SNc) of mice. AS-Ⅳ (20 mg/kg) was intraperitoneally injected once daily for 14 days. Pole and rotarod tests were performed to evaluate behavioral alterations at 32 weeks. Flow cytometry, electrophysiological recordings techniques, and MRI were performed to assess the neuroprotective effects of AS-Ⅳ. AS-Ⅳ ameliorated motor deficits and the incidence of dystonia in animal models of PD. AS-Ⅳ administration inhibited CD4+ T cell activation and increased dopaminergic neurons burst firing. Imaging studies have demonstrated that AS-Ⅳ alters the brain tissue microstructure in the substantia nigra (SN). After administering AS-Ⅳ, bilateral SN fractional anisotropy (FA) values increased, whereas mean diffusivity (MD) values decreased in mice, according to the diffusion tensor imaging (DTI) analysis. In addition, AS-Ⅳ treatment significantly reduced the T2 values of the T2-mapping. In summary, AS-Ⅳ improved motor impairments and efficiently performed neuroprotective functions in the ROT-induced mouse model.

Type 2 Diabetes Mellitus Exacerbates Pathological Processes of Parkinson's Disease: Insights from Signaling Pathways Mediated by Insulin Receptors

Parkinson's disease (PD), a chronic and common neurodegenerative disease, is characterized by the progressive loss of dopaminergic neurons in the dense part of the substantia nigra and abnormal aggregation of alpha-synuclein. Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by chronic insulin resistance and deficiency in insulin secretion. Extensive evidence has confirmed shared pathogenic mechanisms underlying PD and T2DM, such as oxidative stress caused by insulin resistance, mitochondrial dysfunction, inflammation, and disorders of energy metabolism. Conventional drugs for treating T2DM, such as metformin and glucagon-like peptide-1 receptor agonists, affect nerve repair. Even drugs for treating PD, such as levodopa, can affect insulin secretion. This review summarizes the relationship between PD and T2DM and related therapeutic drugs from the perspective of insulin signaling pathways in the brain.

Parkinson's disease screening using a fusion of gait point cloud and silhouette features

Parkinson's Disease (PD) is a neurodegenerative disorder that is often accompanied by slowness of movement (bradykinesia) or gradual reduction in the frequency and amplitude of repetitive movement (hypokinesia). There is currently no cure for PD, but early detection and treatment can slow down its progression and lead to better treatment outcomes. Vision-based approaches have been proposed for the early detection of PD using gait. Gait can be captured using appearance-based or model-based approaches. Although appearance-based gait contains comprehensive features, it is easily affected by factors such as dressing. On the other hand, model-based gait is robust against changes in dressing and external contours, but it is often too sparse to contain sufficient information. Therefore, we propose a fusion of appearance-based and model-based gait features for PD prediction. First, we extracted keypoint coordinates from gait captured in videos and modeled these keypoints as a point cloud. The silhouette images are also segmented from the videos to obtain an overall appearance representation of the subject. We then perform a binary classification of gait as normal or Parkinsonian using a novel fusion of the gait point cloud and silhouette features, obtaining AUC up to 0.87 and F1-Scores up to 0.82 (precision: 0.85, recall: 0.80).