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Objective:To explore the potential application of combining 18F-FDG PET imaging and radiomics in the diagnosis of Parkinson′s disease (PD) and atypical parkinsonian syndromes (APS). Methods:A total of 154 subjects of two cohorts (training set and validation set) were enrolled from Huashan Hospital, Fudan University from March 2015 to August 2020 in this cross-sectional study, including 40 normal controls (NC; 23 males and 17 females, age: (60.2±10.5) years), 40 PD patients (20 males and 20 females, age: (64.7±6.3) years), 40 progressive supranuclear palsy (PSP) patients (20 males and 20 females, age: (64.1±5.9) years), and 34 multiple system atrophy (MSA) patients (19 males and 15 females, age: (65.0±9.2) years). 18F-FDG PET images and clinical scale were selected, and one-way analysis of variance was used to compare differences of clinical scale among groups. Radiomic features extraction and feature selection were carried out. Two and three classification models were constructed based on logistic regression, and the ROC curves of clinical model, radiomics model and combined model were calculated. Independent classification tests were conducted 100 times with 5-fold cross validation in two cohorts. Results:There were significant differences in the scores of unified PD Rating Scale (UPDRS) and Hoehn-Yahr rating scale (H&Y) among different groups in cohort 1 and cohort 2 respectively ( F values: 4.83-17.95, all P<0.05). A total of 2 444 imaging features were extracted from each subject, and after features selection, 15 features for classification were obtained. In the two classification experiment, the AUCs of the three models in binary classification of PD/MSA/PSP/NC group were 0.56-0.68, 0.74-0.93 and 0.72-0.93, respectively. The classification effects of the radiomics model were significantly better than those of the clinical model ( z values: 1.71-2.85, all P<0.05). In the three classification experiment, the sensitivity of the radiomics model reached 80%, 80% and 77% for PD, MSA and PSP, respectively. Conclusion:18F-FDG imaging combined with radiomics has potential in the diagnosis of PD and APS.
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Objective:To reveal the abnormal topology of brain network in Alzheimer′s disease (AD), and evaluate the laterality of tau protein deposition in brains of AD patients based on 18F-APN-1607 PET brain imaging combined with graph theory. Methods:From November 2018 to January 2020, 23 clinically diagnosed AD patients (9 males, 14 females; age (61.3±10.7) years) and 13 normal controls (NC) (9 males, 4 females; age (61.6±4.5) years) who underwent 18F-APN-1607 PET imaging in Huashan Hospital, Fudan University were analyzed in this cross-sectional study. The brain network analysis method based on graph theory was used to construct the tau network of the NC group and the AD group, the network attributes (clustering coefficient, shortest path length, local efficiency, and small-worldness) were calculated, and the asymmetry index (AI) of each group to evaluate the laterality of tau protein deposition was obtained. Permutation test (1 000 times) was used to analyze the differences in brain network parameters between the NC group and the AD group. Results:The tau network of the AD group had obvious topological disorder, and the connections in the olfactory cortex and temporal lobe were weakened, while in the posterior cingulate gyrus, anterior wedge, and parietal occipital lobe, the connections were enhanced. Compared with NC group, clustering coefficient ( t values: 2.28-2.69), local efficiency ( t values: 2.34-3.06) and small-worldness ( t values: 2.26-3.32) were significantly decreased in AD group (all P<0.05) with the sparsity of 20%-50%, while the shortest path length was significantly increased ( t values: 2.13-2.85; all P<0.05). There was significant tau laterality in the posterior cingulate gyrus, superior parietal gyrus, paracentral lobule, superior temporal gyrus and middle temporal gyrus (AI: 10.5%(8.1%, 13.9%), 14.1%(7.6%, 20.3%), -12.4%(-15.7%, -7.8%), -10.8%(-15.3%, -2.1%) , -12.1%(-17.9%, -6.6%), respectively). Conclusion:The tau network analysis based on 18F-APN-1607 may be used to reveal abnormal topological changes of AD patients, and the tau deposition in the posterior cingulate gyrus, superior parietal gyrus, paracentral lobule, superior temporal gyrus and middle temporal gyrus has obvious laterality in AD patients.
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Objective:Exploring tau related disease pattern (tauRDP) in the brain of Alzheimer′s disease (AD) patients based on 18F-APN-1607 PET scan. Methods:18F-APN-1607 PET images were collected from 17 AD patients (6 males and 11 females, age: (61.7±12.3) years, Mini-Mental State Examination (MMSE) score: 17.6±7.9) and 10 normal controls (NC; 6 males and 4 females, age: (61.2±4.7) years) from Huashan Hospital of Fudan University. The scaled subprofile model (SSM) based on principal component analysis (PCA) technique was used to construct the tauRDP. Then the expression value of tauRDP in each sample was calculated. The differences on tauRDP expression values between AD patients and NC were compared by independent-sample t test. Pearson correlation analysis was used to analyze the correlation between tauRDP expression values and MMSE values in AD patients. Results:The tauRDP area mainly included: precentral gyrus, dorsolateral superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus of opercular part, inferior frontal gyrus of triangular part, supplementary motor area, medial superior frontal gyrus, left median cingulate and paracingulate gyri, right cuneus, superior occipital gyrus, middle occipital gyrus, postcentral gyrus, superior parietal gyrus inferior parietal, but supramarginal and angular gyri, supramarginal gyrus, angular gyrus, precuneus and middle temporal gyrus. There were significant differences ( t=4.395, P<0.001) between AD group (12.6±8.0) and NC group (0.0±1.0) in tauRDP expression values. The tauRDP expression values were correlated with MMSE values in AD group significantly ( r=-0.566, P=0.018). Conclusions:TauRDP established basing on SSM/PCA method can be used to quantitatively express the abnormal spatial distributions of tau deposition. Expression value of tauRDP has the potential to initially assess the severity of AD.
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Objective To explore the topological abnormality of brain metabolic network in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and compare it with the topology of brain metabolic network in patients with Parkinson's disease (PD).Methods The 18F-fluorodeoxyglucose (FDG) PET brain images of 19 patients with iRBD diagnosed with polysomnography (PSG) (iRBD group;15 males,4 females,average age:64.9 years),19 patients with PD (PD group;12 males,7 females,average age:62.2 years) and 19 gender and age-matched healthy controls (HC group;15 males,4 females,average age:63.1 years) in Huashan Hospital from September 2014 to June 2015 were retrospectively analyzed.According to the complex brain network method based on graph theory,the brain metabolic networks of each group was constructed and the network parameters (clustering coefficient,characteristic path length,local efficiency,global efficiency and small-world property,etc) were evaluated quantitatively.The 500 times non-parametric permutation test was used to determine the differences in network parameters between groups.Results The brain metabolic networks of iRBD group and PD group both had abnormal topological structure,which showed that the characteristic path length (for example,when sparsity =34％,HC vs iRBD vs PD groups:1.517 vs 1.552 vs 1.561) and local efficiency (for example,when sparsity=30％,HC vs iRBD vs PD groups:0.802 vs 0.824 vs 0.831) were significantly increased (both P＜0.05),the global efficiency (for example,when sparsity =36％,HC vs iRBD vs PD groups:0.672 vs 0.658 vs 0.656) was significantly decreased (P＜0.05).The topology was more aggravated in PD group compared with that in iRBD group.Conclusion The graph-based complex brain network analysis can reveal the abnormal topological structure of the brain metabolic network in which iRBD progresses to PD.
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Objective@#To explore the topological abnormality of brain metabolic network in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and compare it with the topology of brain metabolic network in patients with Parkinson′s disease (PD).@*Methods@#The 18F-fluorodeoxyglucose (FDG) PET brain images of 19 patients with iRBD diagnosed with polysomnography (PSG) (iRBD group; 15 males, 4 females, average age: 64.9 years), 19 patients with PD (PD group; 12 males, 7 females, average age: 62.2 years) and 19 gender and age-matched healthy controls (HC group; 15 males, 4 females, average age: 63.1 years) in Huashan Hospital from September 2014 to June 2015 were retrospectively analyzed. According to the complex brain network method based on graph theory, the brain metabolic networks of each group was constructed and the network parameters (clustering coefficient, characteristic path length, local efficiency, global efficiency and small-world property, etc) were evaluated quantitatively. The 500 times non-parametric permutation test was used to determine the differences in network parameters between groups.@*Results@#The brain metabolic networks of iRBD group and PD group both had abnormal topological structure, which showed that the characteristic path length (for example, when sparsity=34%, HC vs iRBD vs PD groups: 1.517 vs 1.552 vs 1.561) and local efficiency (for example, when sparsity=30%, HC vs iRBD vs PD groups: 0.802 vs 0.824 vs 0.831) were significantly increased (both P<0.05), the global efficiency (for example, when sparsity=36%, HC vs iRBD vs PD groups: 0.672 vs 0.658 vs 0.656) was significantly decreased (P<0.05). The topology was more aggravated in PD group compared with that in iRBD group.@*Conclusion@#The graph-based complex brain network analysis can reveal the abnormal topological structure of the brain metabolic network in which iRBD progresses to PD.
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In aging society the development of non-invasive continuously blood pressure monitors which are suitable for homes, communities and nursing homes has a wide range of applications. This paper proposes a non-invasive continuously blood pressure monitoring based on wearable device which uses MSP430F5529 as the central processor. The design is divided into signal acquisition module, central control module, display module, power supply module and host computer module. The experimental results showed that DBP (375/390, 96.15%) and SBP estimation values (377/390, 96.67%) are in 95% confidence interval, which means our design passes Bland-Altman test with high accuracy and stability.
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Blood Pressure , Blood Pressure Determination , Blood Pressure Monitors , Electric Power Supplies , Wearable Electronic DevicesABSTRACT
With the advent of social aging, the development of intelligent multifunctional nursing beds that are suitable for hospitals, nursing homes, homes and the like has a wide range of applications, this paper presents an intelligent nursing bed design based on Internet of Things technology. The design uses STM32F103 as the central processor. The design is divided into nursing bed module based on tri-fold structure, central control module based on data processing, weight scale module based on weight detection, power supply module based on system power supply and host computer module based on user operation. The design uses a closed control mode, greatly improving the bed control accuracy. Experimental tests showed that under the action of the intelligent control bed control system, the error rate of bed position information driven bedboard can be less than 2%, which has high accuracy and stability.