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Background Studies of glymphatic dysfunction in Parkinson’s disease (PD) patients have attracted much attention in recent years. However, the relationships between glymphatic dysfunction and clinical symptoms remains unclear. Objectives To determine whether the diffusion tensor image analysis along the perivascular space (DTI-ALPS) affect the severity and types of motor and non-motor symptoms in PD patients. Methods De novo PD patients and controls who performed both DTI and 123 I-DaTscan single photon emission computed tomography (SPECT) scanning were retrieved from the international multicenter Parkinson’s Progression Marker Initiative (PPMI) cohort. Glymphatic system was evaluated by the DTI-ALPS. Motor symptoms were assessed by Movement Disorders Society Unified Parkinson’s Disease Rating Scale III (MDS-UPDRS-III). The influence of glymphatic activity on motor and non-motor symptoms was explored by multivariate linear regression models. Results A total of 153 PD patients (mean age 60.97 ± 9.47 years; 99 male) and 67 normal controls (mean age 60.10 ± 10.562 years; 43 male) were included. The DTI-ALPS index of PD patients was significantly lower than normal controls ( Z  = − 2.160, p  = 0.031). MDS-UPDRS III score ( r  = − 0.213, p  = 0.008) and subscore for rigidity ( r  = − 0.177, p  = 0.029) were negatively correlated with DTI-ALPS index. The DTI-ALPS index was significantly associated with MDS-UPDRS-III score ( β  = − 0.160, p  = 0.048) and subscore for rigidity ( β  = − 0.170, p  = 0.041) after adjusting for putamen dopamine transporter availability and clinical factors. Conclusions Our results showed distinct relationships between glymphatic dysfunction and the severity and types of PD motor symptoms, suggesting the potential of DTI-ALPS index as a biomarker for PD motor symptoms.

Accurate localization of brain regions responsible for language and cognitive functions in epilepsy patients is important. Electrocorticography (ECoG)-based real-time functional mapping (RTFM) has been shown to be a safer alternative to electrical cortical stimulation mapping (ESM), which is currently the clinical/gold standard. Conventional methods for analyzing RTFM data mostly account for the ECoG signal in certain frequency bands, especially high gamma. Compared to ESM, they have limited accuracy when assessing channel responses. In the present study, we developed a novel RTFM method based on tensor component analysis (TCA) to address the limitations of current estimation methods. Our approach analyzes the whole frequency spectrum of the ECoG signal during natural continuous speech. We construct third-order tensors that contain multichannel time-frequency information and use TCA to extract low-dimensional temporal, spectral and spatial modes. Temporal modulation scores (correlation values) are then calculated between the time series of voice envelope features and TCA-estimated temporal courses, and significant temporal modulation determines which components' channel weightings are displayed to the neurosurgeon as a guide for follow-up ESM. In our experiments, data from thirteen patients with refractory epilepsy were recorded during preoperative evaluation for their epileptogenic zones (EZs), which were located adjacent to the eloquent cortex. Our results showed higher detection accuracy of our proposed method in a narrative speech task, suggesting that our method complements ESM and is an improvement over the prior RTFM method. To our knowledge, this is the first TCA-based method to pinpoint language-specific brain regions during continuous speech that uses whole-band ECoG.

Studies were selected for the review if they included (1) participants meeting a diagnosis of idiopathic PD, as defined by the UK Parkinson Disease Society Brain Bank Criteria or the Movement Disorders Society (MDS) diagnostic criteria; (2) a specific light intervention, (3) a randomized trial design with a control condition; and (4) outcome measures of PD symptoms. [...]only a few RCTs assessed the efficacy of LT on PD. To our knowledge, this is the first systematic review and meta-analysis to address the effect of LT in the management of PD. For the analysis, we did not find any significant difference in motor function, depression, daytime sleepiness, fatigue, and QoL between LT and controls. [8] Our effect size is approaching the MCRD. [...]we considered it of potential clinical importance, which means that the evidence of LT to improve daytime sleepiness in patients with ESS scores > 10 (severe daytime sleepiness symptom) might be put in a real clinical situation. [...]long-term impact of LT on PD was not investigated based on the current evidence. [...]our findings suggest that LT might be of value specifically to treat PD patients with severe daytime sleepiness symptom (ESS > 10) or be used to improve the sleep quality of PD. LT might be of value specifically to treat PD patients with more severe daytime sleepiness associated with poor sleep quality.

In traditional epistemic logic—particularly modal logic—agents are often assumed to have complete and certain knowledge, which is unrealistic in real-world scenarios where uncertainty, imprecision, and the incompleteness of information are common. This study proposes an extension of the logic of doxastic attitudes to a fuzzy setting, representing beliefs or knowledge as continuous values in the interval [0, 1] rather than binary Boolean values. This approach offers a more nuanced and realistic modeling of belief states, capturing the inherent uncertainty and vagueness in human reasoning. We introduce a set of axioms for the fuzzy logic of doxastic attitudes, formalizing how agents reason with regard to uncertain beliefs. The theoretical foundations of this logic are established through proofs of soundness and completeness. To demonstrate practical utility, we present a concrete example, illustrating how the fuzzy logic of doxastic attitudes can model uncertain preferences and beliefs.

Frontline medical workers (FMWs) faced high stress levels in frontline clinics during the COVID-19 pandemic. This study aims to alleviate FMWs’ psychological stress by enhancing protection measures, which includes refining the selection process, improving protective measures for their work in frontline clinics, and determining the appropriate time for evacuation. A Chinese-language-version stress questionnaire, covering stress-related issues and sociodemographic characteristics, was administered to FMWs in Shanghai during the COVID-19 pandemic in 2022, yielding 107 valid responses. The questionnaire’s reliability and validity were subsequently confirmed. Exploratory factor analysis (EFA) was applied to the stress-related questions, with the KMO and Bartlett’s test values of 0.886, 877.8522 (df = 120, p  < 0.0001), respectively. Structure equation modeling (SEM) was used to confirm the factors and measure the relationships among the stress-related questions, factors and sociodemographic characteristics. The parameters of SEM named SOFAIDC were significant with SRMSR, RMSEA, AGFI, NFI, and PNFI being 0.0422, 0.0507, 0.9946, 0.9958, and 0.7615, respectively. Paired sample t-tests revealed the significant decrease in FMWs’ stress score of the current week and the previous week, with all t-values being negative and significant ( p  < 0.01). A latent growth model (LGM) named LGM30 verified the overall decreasing stress trend over the weeks, with high R-squares (> 0.80), significant positive linear and negative quadratic time parameters. Based on the trajectory analysis, a stress scorecard with an AUC of 0.856 was developed. This study found that there are four factors for FMWs’ psychological stress (anxiety about infection, exhaustion, lack of cognition for infection, and hesitant feeling), and improving targeted hospital protection could alleviate FMWs’ psychological stress through SOFAIDC, as all the total effect of factors or items from the feeling of being protected by hospital were negative. Meanwhile, although overall FMWs’ stress trend has decreased, 22.43% of FMWs have maintained or experienced a high stress trend, and the stress might increase over weeks in frontline clinics. To perfect hospital protection to alleviate FMWs’ psychological stress, the scorecard could be used to select FMWs with low-stress trends, LGM30 could be used to determine the withdrawal time (such as week 8), and targeted hospital protections could be designed based on the relationships between feeling of being protected by the hospital and factors in SOFAIDC, such as providing adequate protective equipment, improving the rationality of FMWs’ work schedules, recruiting personnel with higher education levels, and paying adequate monetary compensation.

Genetic variants of dopaminergic transcription factor-encoding genes are suggested to be Parkinson's disease (PD) risk factors; however, no comprehensive analyses of these genes in patients with PD have been undertaken. Therefore, we aimed to genetically analyze 16 dopaminergic transcription factor genes in Chinese patients with PD. Whole-exome sequencing (WES) was performed using a Chinese cohort comprising 1917 unrelated patients with familial or sporadic early-onset PD and 1652 controls. Additionally, whole-genome sequencing (WGS) was performed using another Chinese cohort comprising 1962 unrelated patients with sporadic late-onset PD and 1279 controls. We detected 308 rare and 208 rare protein-altering variants in the WES and WGS cohorts, respectively. Gene-based association analyses of rare variants suggested that MSX1 is enriched in sporadic late-onset PD. However, the significance did not pass the Bonferroni correction. Meanwhile, 72 and 1730 common variants were found in the WES and WGS cohorts, respectively. Unfortunately, single-variant logistic association analyses did not identify significant associations between common variants and PD. Variants of 16 typical dopaminergic transcription factors might not be major genetic risk factors for PD in Chinese patients. However, we highlight the complexity of PD and the need for extensive research elucidating its etiology.

Autonomic dysfunction (AutD) is one of the non-motor symptoms (NMSs) in Parkinson’s disease (PD). To investigate the prevalence and clinical features of AutD in Chinese patients with PD, a large multicenter cohort of 2,556 individuals with PD were consecutively involved in the Parkinson’s Disease and Movement Disorders Multicenter Database and Collaborative Network in China (PD-MDCNC) between January 1, 2017, and December 31, 2019. The assessment of AutD was performed using the Scale for Outcomes in Parkinson’s Disease for Autonomic Symptoms (SCOPA-AUT). The evaluation of motor symptoms and other NMSs were performed using well-established scales recommended by the Movement Disorder Society. We found that out of 2,556 patients with PD, 2,333 patients with PD (91.28%) had AutD. Compared with the group of patients with PD without AutD, the group of patients with PD with AutD had older age, older age of onset, longer disease duration, more severe motor symptoms, motor complications, and more frequent NMSs. As for partial correlation analysis, the total SCOPA-AUT score was significantly and positively associated with motor severity scales [Unified Parkinson’s Disease Rating Scale (UPDRS) total score] and some of the NMSs [Rapid Eye Movement Sleep Behavior Disorder Questionnaire (RBD), Epworth Sleepiness Scale, Hamilton Depression Scale], Fatigue Severity Scale, and Parkinson’s disease questionnaire. PD Sleep Scale was significantly and negatively correlated with AutD. With logistic regression analysis for potentially related factors, age, UPDRS total score, RBD, hyposmia, depression, and fatigue may be associated with PD with AutD. In conclusion, our multicenter cohort study reported the high prevalence of AutD in Chinese PD and revealed the associated factors of PD with AutD.

The continuous consumption of fossil energy and excessive emissions of carbon dioxide (CO2) have caused a serious energy crisis and led to the greenhouse effect. Using natural resources to convert CO2 into fuel or high-value chemicals is considered to be an effective solution. Photoelectrochemical (PEC) catalysis utilizes abundant solar energy resources, combined with the advantages of photocatalysis (PC) and electrocatalysis (EC), to achieve efficient CO2 conversion. In this review, the basic principles and evaluation criteria, of PEC catalytic reduction to CO2 (PEC CO2RR), are introduced. Next, the recent research progress on typical kinds of photocathode materials for CO2 reduction are reviewed, and the structure–function relationships between material composition/structure and activity/selectivity are discussed. Finally, the possible catalytic mechanisms and the challenges of using PEC to reduce CO2 are proposed.

Metal-based nanoparticles (MNPs) are increasingly prevalent in the environment due to both natural processes and human activities, leading to direct interactions with plants through soil, water, and air exposure that can have beneficial and detrimental effects on plant growth and health. Understanding the uptake, translocation, and transformation of MNPs in plants is crucial for assessing environmental risks and leveraging nanotechnology in agriculture. However, accurate analysis of MNPs in plant tissues poses significant challenges due to complex plant matrices and the dynamic nature of nanoparticles. This short review summarizes recent advances in analytical methods for determining MNP–plant interactions, focusing on pre-processing and quantitative nanoparticle analysis. It highlights the importance of selecting appropriate extraction and analytical techniques to preserve nanoparticle integrity and accurate quantification. Additionally, recent advances in mass spectrometry, microscopy, and other spectroscopic techniques that improve the characterization of MNPs within plant systems are discussed. Future perspectives highlight the need to develop real-time in situ monitoring techniques and sensitive tools for characterizing nanoparticle biotransformation.