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Background The diagnosis of Parkinson disease (PD) is mainly based on clinical symptoms, and there is a lack of objective methods for correct diagnosis. At present, there have been studies on retinal structural changes as a biomark for early diagnosis of PD, but machine learning based on retinal structural changes for predicting early PD has not yet been studied. Objective To construct a machine learning model based on the characteristics of retinal structural changes, explore its value in early PD diagnosis, and the accuracy of different machine learning algorithms for early PD diagnosis. Methods From October 2021 to September 2022, 49 PD patients aged 40 to 70 years old (PD group) who attended outpatient clinics and were hospitalized in the department of neurology of Henan Provincial People's Hospital (PD group) and 39 healthy people with matching age and sex (healthy control group) who came to the hospital for physical examination were collected. All study subjects underwent swept-source optical coherence tomography and swept-source optical coherence tomography angiography, the thickness and vessel density of the macular retina were also quantitatively analyzed. The 88 subjects were randomly divided into the 62 training sets and 26 validation set according to the ratio of 7∶3. Variables with significant differences between the PD group and healthy control group were selected as the characteristic variables for inclusion in the machine learning model, and Logistic regression (LR) , K-nearest neighbor algorithm (KNN) , decision tree (DT) , random forest (RF) and extreme gradient boosting (XGboost) models were constructed in the training set. The area under the curve (AUC) , accuracy, sensitivity and specificity of the receiver operating characteristic (ROC) curve were used to evaluate the predictive value of the machine learning model based on retinal structural changes for early PD. Results Compared with the healthy control group, the density of the upper outer ring (A6) , the outer temporal outer ring (A7) , the lower outer ring (A8) and the outer nasal ring (A9) of the superficial capillaries in the PD group were reduced, the thickness of the upper inner ring (A2) , the inner temporal inner ring (A3) , the inferior inner ring (A4) , the inner ring of the nasal side (A5) of the retinal layer, A6, A7, A8 and A9, the thickness of A6 of the ganglion cell complex layer, the thickness of A7 of the nerve fiber layer, A2 and A4, A5, A6, A7, A8, A9 became thinner (P

Background Parkinson's disease (PD) is frequently accompanied with anxiety, depression, sleep disorders, autonomic dysfunction, cognitive impairment, and other non-motor symptoms, among which autonomic dysfunction and cognitive impairment severely impair the quality of life. The relationship between autonomic dysfunction and cognitive impairment in PD is still controversial. Objective To analyze the characteristic of autonomic dysfunction and its correlation with cognitive impairment in PD patients. Methods A total of 156 patients with idiopathic PD admitted to Henan Provincial People's Hospital between September 2018 and November 2020 were enrolled. General data of patients, including age, sex, duration of disease, age of onset and years of education, were collected. According to the prevalence and duration of autonomic dysfunction, they were divided into autonomic dysfunction group (AutD group, n=107) and non-autonomic dysfunction group (NAutD group, n=49) . The part Ⅲ of the Unified Parkinson's Disease Rating Scale (UPDRS-Ⅲ) was used to assess motor symptoms. The Hoehn-Yahr (H-Y) scale was used to assess the severity of PD. The non-motor symptoms were assessed by the Non-motor Symptoms Scale (NMSS) . The Hamilton Anxiety Scale (HAMA) and Hamilton Rating Scale for Depression 24-item (HAMD-24) were used to evaluate anxiety and depression symptoms, respectively. The 39-item Parkinson's Disease Questionnaire (PDQ-39) was used to evaluate PD-specific health related quality of life. Autonomic function was evaluated by Scales for Outcomes in Parkinson's disease-Autonomic (SCOPA-AUT) . Cognitive function was evaluated by Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) . Spearman correlation analysis was used to explore the correlation between autonomic function (assessed using the SCOPA-AUT score) and cognitive function (assessed using the MMSE score and MoCA score) . Results Among the 156 patients, 83 were male and 73 were female, with a mean age of (63.9±0.7) years. AutD group had greater mean age (t=-4.391, P

This study aims to analyse hyperechoic substantia nigra (HSN) characteristics and the correlation of HSN with clinical features and blood biomarkers in patients with Parkinson's disease (PD). Transcranial sonography (TCS) evaluations of the substantia nigra (SN) were performed in 40 healthy controls and 71 patients with PD, including patients with SN hyperechogenicity (SN+) and those with normal SN echogenicity (SN−). Evaluation of motor and non‐motor symptoms was assessed by a series of rating scales. The uricase method was used to determine serum uric acid (UA) levels, and enzyme‐linked immunosorbent assay (ELISA) was used to measure plasma interleukin (IL)‐1β levels. TCS showed 92.50% specificity and 61.97% sensitivity in differentiating PD patients from controls. The area of SN+ contralateral to the side of initial motor symptoms (SNcontra) was larger than that ipsilateral to the side of initial motor symptoms (SNipsi). The PDSN+ group had lower Argentine Hyposmia Rating Scale (AHRS) scores and UA levels than the PDSN− group. Binary logistic regression analysis revealed that AHRS scores and UA levels could be independent predictors for HSN. The larger SN echogenic area (SNL) sizes positively correlated with plasma IL‐1β levels in PD patients with SN+. The present study provides further evidence of the potential of SN echogenicity as an imaging biomarker for PD diagnosis. PD patients with HSN have more severe non‐motor symptoms of hyposmia. HSN in PD patients is related to the mechanism of abnormal iron metabolism and microglial activation. [ABSTRACT FROM AUTHOR]

Objective: This study was designed to investigate the diagnostic value of plasma SIRT1 levels and whole-brain gray matter (GM) volume in Parkinson's disease (PD) patients with cognitive impairment. Methods: Automated enzymatic analysis was performed to measure plasma SIRT1 levels in 80 healthy controls and 77 PD patients. Motor symptoms and nonmotor symptoms in PD patients were assessed using the corresponding scales. A Siemens MAGNETOM Prisma 3 T MRI scanner was used to acquire images in 35 of 77 PD patients. Results: Plasma SIRT1 levels in PD patients were lower than those in healthy controls. Plasma SIRT1 levels were negatively correlated with the age, Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) scores, anxiety, depression, excessive daytime sleepiness (EDS), quality of life, and especially cognitive impairment. Thus, it showed that plasma SIRT1 levels were relevant to visuospatial/executive function, memory, and language. Receiver-operating characteristic (ROC) analysis confirmed that plasma SIRT1 levels had good diagnostic accuracy for PD with anxiety and EDS. Furthermore, plasma SIRT1 levels had a significant positive correlation with GM volume in the whole brain, and ROC analysis confirmed that plasma SIRT1 levels and the total GM volume had good diagnostic accuracy for PD with cognitive impairment. Conclusions: This study showed that plasma SIRT1 levels were correlated with the nonmotor symptoms of anxiety, depression, EDS, and especially cognitive impairment as well as the total GM volume. Furthermore, the combination of plasma SIRT1 levels and the total GM volume had good diagnostic accuracy for PD with cognitive impairment. [ABSTRACT FROM AUTHOR]

The topic of this paper is to study the numerical solution of the fourth-order partial differential equation and analyze its visual application in software simulation. Therefore, for the initial circular domain, the expansion should be transformed into a fourth-order problem in the plane dimension. Then, we introduced appropriate-weighted Sobolev space based on the improved neural network algorithm and established a weak form and the corresponding discrete form for each one-dimensional fourth-order problem. The approximation properties of the cubic Hermite interpolation operator are used to verify the error value of the approximation solution. After obtaining the relevant algorithms, numerical empirical analysis is carried out to prove that the proposed algorithm is effective. Therefore, this article applies it to the visualization simulation technology, and the visualization module mainly completes two tasks: collecting geometric data and drawing models. At present, the application of the visualization module in the program mainly has two aspects: on the one hand, the boundary information of geometry can be obtained by screening the existing database so that the boundary model can be displayed in the visualization; on the other hand, from the calculation result file. Read the geometric information of particles and boundaries and perform dynamic simulation playback of the calculated results. [ABSTRACT FROM AUTHOR]

Abstract: This paper obtains some sufficient conditions for the relative ϕ 0-stability of the two impulsive hybrid systems by using the method of perturbing Lyapunov functions. [Copyright &y& Elsevier]

• This study showed that in Chinese PD patients, old age, low education level, female sex, and severe motor symptoms were risk factors for cognitive impairment. • Decreased UCH-L1 levels and elevated NfL levels in the blood were related to cognitive decline in PD patients; additionally, this study is the first to report a negative correlation between UCH-L1 and NfL levels. • We found that UCH-L1 and NfL levels had high sensitivity and specificity in the diagnosis of cognitive impairment in Parkinson's disease, especially UCHL1 levels. • These proteins are promising biomarkers for diagnosing cognitive impairment in Parkinson's disease. Ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and Neurofilament light chain (NfL) are associated with Lewy body formation, Lewy bodies are the main pathological feature of Parkinson's disease (PD). The relationship between UCH-L1 and PD cognition remains unclear, and NfL is an important marker of cognitive impairment. The aim of this study is to investigate the relationship among serum UCH-L1 levels, plasma NfL levels and cognitive dysfunction in PD patients. There were significant differences in UCH-L1 and NfL levels among PD patients with normal cognitive function (PD-CN), PD patients with mild cognitive impairment (PD-MCI), and PD-dementia patients (PDD) (P < 0.001; P < 0.001). The PDD group had lower levels of UCH-L1 (Z = 6.721, P < 0.001; Z = 7.577, P < 0.001) and higher levels of NfL (Z = −3.626, P = 0.001; Z = −2.616P = 0.027) than the PD-NC and PD-MCI groups. Serum UCH-L1 levels were positively correlated with MMSE scores, MoCA scores, and its subitems in PD patients (P < 0.001), and plasma NfL levels were negatively correlated with MMSE scores, MoCA scores, and its items (P < 0.01) (except for "abstract"). Decreased UCH-L1 levels and elevated NfL levels in the blood are associated with cognitive dysfunction in PD; thus, these proteins are potential biomarkers for the diagnosis of cognitive dysfunction in PD patients. [ABSTRACT FROM AUTHOR]