Objective To explore the association between acute ischemic stroke and albumin to creatinine ratio (ACR).Methods A case-control study.During January to December in 2013, 127 acute ischemic stroke patients as case group and 150 controls who were similar with case group in age and gender were recruited in Tianjin Union Medicine Center . According to diabetes , hypertension , cardiovascular diseases and patients without these three diseases , case group was divided into A1, B1, C1 and D1 subgroups, control group was divided into A2, B2, C2 and D2 subgroups in the same way.The first morning urine from participants were collected .Urinary albumin concentration was tested by nephelometry , urinary creatinine was examined by using enzymic method , ACR were calculated(≥30 mg/g as the cutoff value). Then difference of ACR between case and control group was compared , the cutoff value of albuminuria for ischemic stroke patients was analyzed by ROC , and the risk factor of ischemic stroke were analyzed by logistic regression analysis.Results The prevalence of albuminuria in ischemic stroke patients was 38.58%(49).According to analysis of covariance, after adjustment for age, gender, cardiac-disease, diabetes, hypertension, lnACR in case group was significantly higher than control group (3.18 mg/g vs 2.78 mg/g, t=2.13 P=0.03), especially D1 was significantly higher than D2 subgroup (3.01 mg/g vs 2.51 mg/g,t=5.56,P=0.009) .If 19.82 mg/g from ROC analysis was used as the cut-off value, the sensitivity and specificity are 68.5% and 61.7%.The odds ratio ( OR ) of ACR >19.82 mg/g was about 2-fold when compared with ACR<19.82 mg/g adjusted for stroke risk factors , and the OR value is 2.43 in comparison of patients without diabetes , hypertension and cardiovascular diseases .Conclusions Urinary albumin excretion is the independent risk factor of ischemic stroke .The increased urinary albumin has important clinical significance to predict the risk of ischemic stroke for the patients without diabetes , hypertension and cardiovascular diseases.

Objective To investigate the situation of urine albumin measurement of clinical laboratory in Tianjin.Methods Control materials from patient mixed urine samples were made to validate precision in the clinical laboratories in Tianjin.Reference Material ERM-DA470K was prepared as the first external quality assessment ( EQA) sample, and the bias between laboratories was calculated.Then we give some advice about the methods of routine maintenance, calibration, standardized operation, internal quality assessment and so on to the laboratories which were not qualified in the first EQA.Then the second EQA was carried out and CV and bias were culculated.Results 52 clinical laboratories has 12 series of instruments and 17 series of reagents.The precision research showed that most laboratories ( 93.55%) had good precision for urine albumin measurement, while CV of inter-laboratory was great:the range of low level of control sample was 8.91 -43.95 mg/L, 34.46% for CV; and the high level was 36.32 -281 mg/L, 28.51% for CV.Only 36.5% laboratories were qualified in the first EQA. The qualified rate for nephelometry and turbidimetry was higher (55.6%, 42.9%).The qualified rate of trueness verification was 58.6%in the second EQA, and the CV between laboratories was significantly decreased, Inter-laboratory CV of the five samples were:19.83%, 13.57%, 13.41%, 13.08%, 11.37%. The qudified rate for nephelomety and turbidimetry was 71.4% and 56.3%.Conclusions There are a mide variety of measurement systems of urine albumin in Tianjin, and the CV between these systems is great.Clinical laboratory should strengthen the laboratory standardization operation and upgrade calibration testing to improve the testing consistency.

The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD
Alzheimer Disease/diagnosis* ; Brain/diagnostic imaging* ; Cognitive Dysfunction/diagnosis* ; Early Diagnosis ; Humans ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Neurodegenerative Diseases

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that damages patients' memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neural Networks, Computer ; Magnetic Resonance Imaging/methods* ; Neuroimaging/methods* ; Diagnosis, Computer-Assisted ; Brain ; Cognitive Dysfunction