Background: Parkinson's disease (PD) patients exhibit deficits in oculomotor behavior, yet the results are inconsistent across studies. In addition, how these results are associated with clinical symptoms is unclear, especially in China. Methods: We designed a case-control study in China including 37 PD patients and 39 controls. Clinical manifestations in PD patients were recorded. Oculomotor performance was measured by a video-based eye tracker system. Results: We found that six oculomotor parameters, including fixation stability, saccadic latency, smooth pursuit gain, saccade frequency, viewing range, and saccade frequency during free-viewing context, were significantly different in PD patients and control group. Combining application of these six parameters could improve diagnostic accuracy to over 90%. Moreover, pursuit gain was significantly associated with PD duration, UPDRS III, in PD patients. Saccade latency was significantly associated with PD duration, Berg balance score, RBD score, and Total LEDD in PD patients. Conclusions: PD patients commonly exhibit oculomotor deficits in multiple behavioral contexts, which are associated with both motor and non-motor symptoms. Oculomotor test may provide a valuable tool for the clinical assessment of PD.

Background: Masked face is a characteristic clinical manifestation of Parkinson disease (PD), but subjective evaluations from different clinicians often show low consistency owing to a lack of accurate detection technology. Hence, it is of great significance to develop methods to make monitoring easier and more accessible.Objective: The study aimed to develop a markerless 2D video, facial feature recognition–based, artificial intelligence (AI) model to assess facial features of PD patients and investigate how AI could help neurologists improve the performance of early PD diagnosis.Methods: We collected 140 videos of facial expressions from 70 PD patients and 70 matched controls from 3 hospitals using a single 2D video camera. We developed and tested an AI model that performs masked face recognition of PD patients based on the acquisition and evaluation of facial features including geometric and texture features. Random forest, support vector machines, and k-nearest neighbor were used to train the model. The diagnostic performance of the AI model was compared with that of 5 neurologists.Results: The experimental results showed that our AI models can achieve feasible and effective facial feature recognition ability to assist with PD diagnosis. The accuracy of PD diagnosis can reach 83% using geometric features. And with the model trained by random forest, the accuracy of texture features is up to 86%. When these 2 features are combined, an F1 value of 88% can be reached, where the random forest algorithm is used. Further, the facial features of patients with PD were not associated with the motor and nonmotor symptoms of PD.Conclusions: PD patients commonly exhibit masked facial features. Videos of a facial feature recognition–based AI model can provide a valuable tool to assist with PD diagnosis and the potential of realizing remote monitoring of the patient’s condition, especially during the COVID-19 pandemic.

Objectives: Our study aimed to investigate the prevalence of cognitive impairment(CI) and the associated risk factors among elderly people in Shanghai urban area, China. Methods: A population-based survey was conducted among people aged 55 years or older in urban areas of Shanghai. Face-to-face interviews were carried out to collect information including demographic characteristics, medical history, and medication use, etc. The validated Chinese version of the Mini-Mental State Examination(MMSE) was used to screen subjects with CI, and the criteria of CI were adjusted for education levels. Results: A total of 3,176 home-living residents (≥55 years old) were included in the study. Among them, 266 people (102 men and 164 women) were identified as cognition impaired, with a prevalence of 8.38% (266/3,176, 95% CI: (8.26, 8.49)) for both genders, 9.21% (102/1,107,95% CI: (9.18, 9.33)) for men and 7.93% (164/2,069, 95% CI: (7.80, 8.09)) for women, respectively. Furthermore, we found that several significant risk factors, including social factors(education, number of children, marriage status, and family structure), physiological factors (age, blood glucose level, and obesity), factors on living styles(physical exercise, diet & chronic diseases), and genetic factor(ApoE), associated with CI onset. Conclusions: This study confirms the high prevalence of CI among the elderly population in the Shanghai urban in China, similar to previous epidemiologic studies in Western countries. The putative risk factors associated with CI merit further investigated.

Objective: The objective of this study was to assess the relationship between white matter hyperintensities (WMH) and the occurrence and progression of apathy in Parkinson’s disease (PD).Methods: We recruited patients with PD who underwent baseline evaluation, which included apathy assessment and magnetic resonance imaging (MRI) head scans. After 2.5 years of follow‐up, we re‐evaluated patient apathy symptoms. The severity and location of WMH were assessed with fluid‐attenuated inversion recovery (FLAIR) sequences using the Fazekas visual rating scale. Logistic regression and linear regression analyses of baseline WMH characteristics were conducted to explore the potential association between apathy and WMH.Results: A total of 141 PD patients were recruited. The apathy group had a higher proportion of male patients, advanced disease, and depression, which was coupled with a lower quality of life. Morever, higher WMH severity was significantly associated with apathy. Logistic regression analyses demonstrated that WMH severity was a risk factor for apathy. In addition, linear regression analysis also suggests that apathy severity is positively correlated with baseline WMH Fazekas scales (ϐ = 0.959, P < 0.001). Baseline WMH severity was also a risk factor for apathy progression.Interpretation: WMH is associated with apathy and could be a promising marker to predict apathy progression in PD.

Background: Glutaric acidemia type II (GA II) or multiple acyl-CoA dehydrogenase deficiency (MADD, OMIM 231680) is an inherited autosomal recessive disease affecting fatty acid, amino acid and choline metabolism, due to mutations in one of three genes namely, electron transfer flavoprotein alpha-subunit, ETFA, electron transfer flavoprotein β-subunit, ETFB and electron transfer flavoprotein dehydrogenase, ETFDH. Currently, few studies have reported genetic profiling of neonatal-onset GA II. This study aimed to identify the genetic mutations in a Chinese family with GA II. Case presentation: We reported a case of GA II with purulent meningitis and septicemia and identified a novel ETFDH gene mutation in a female infant. The patient developed an episode of hypoglycemia and hypotonicity on the postnatal first day. Laboratory investigations revealed elevations of multiple acylcarnitines indicating glutaric acidemia type II in newborn screening analysis. Urinary organic acids were evaluated for the confirmation and revealed a high glutaric acid excretion. Genetic analysis revealed two mutations in the ETFDH gene (c.623_626 del / c. 1399G > C), which were considered to be the etiology for the disease. The novel mutation c.623_626 del was identified in the proband infant and her father, her mother was carriers of the mutation c.1399G > C. Conclusions: A novel variant (c.623_626 del) and a previously reported missense (c.1399G > C) in the ETFDH gene have been identified in the family. The two variants of ETFDH gene identified probably underlie the pathogenesis of Glutaric acidemia type II in this family, and also enlarge ETFDH genotype-phenotype correlations spectrum.