OBJECTIVE:To evaluate the efficacy of latanoprost for patients with refractory angle-closure glaucoma in whom the intraocular tension failed to be kept under control by common anti-glaucoma drugs.METHODS:A total of 23 refractory glaucoma cases(36 eyes)whose intraocular pressure(IOP)failed to be kept under control by common anti-glaucoma drugs were assigned to receive latanoprost alone or latanoprost combined with timolol or pilocarpine for 6 months.The intraocular tension,visual acuity,visual field and side-effect were observed.RESULTS:In acute primary angle-closure glaucoma group,the mean IOP was(27.13?5.21)mmHg before treatment vs.(16.21?3.45)mmHg at six months(P

Objective To explore the factors influencing pneumococcal vaccination uptake among the el-derly people in Guangzhou. Methods A survey by questionnaire was performed among 827 subjects aged 60 years or above and living in Guangzhou for five consecutive years. Chi-square test and multiple logistic regression analysis were applied to identify factors influencing pneumococcal vaccination uptake among the elderly. Results The positive factors for vaccination uptake among the elderly people included age of over 70 years (OR=1.677, 95%CI: 1.156 ~ 2.434), mental workers (OR = 1.837, 95%CI: 1.214 ~ 2.779), education background of over-three-year-course training (OR=1.769, 95%CI:1.039~3.012), and history of chronic diseases (OR=1.659, 95%CI:1.096~2.512) were positively associated with pneumococcal vaccination uptake. Monthly disposable income was not an influencing factor (OR=1.420, 95%CI: 0.895 ~ 2.251). Conclusion Strengthened publicity of pneumo-coccal vaccination among the elderly people and flexible measures tailored to the needs of different groups are rec-ommended in order to improve pneumococcal vaccination uptake among the elderly people.

Objective To investigate the levels of sICAM 1 and sE selectin in patients with chronic hepatitis(CHC) and to study their roles in judge of response to IFN ? 2b treatment. Methods sICAM 1 and sE selectin levels were measured in 32 cases of CHC before and after treatment of IFN ? 2b by enzyme linked immunosorbent assay(ELISA), levels of HCV RNA was detected by quantitative PCR and serum ALT activity was also detected. Results Levels of sICAM 1 and sE selectin in CHC patients were significantly higher than those in normal controls(P

A fitting method of calculating local helix parameters of proteins based on dual quaternions registration fitting (DQRFit) is proposed in this paper. First, the C and N atom coordinates of each residue in the protein structure data are extracted. Then the unregistered data and reference data are constructed using the sliding windows. The square sum of the distance of the data points before and after registration is regarded as an optimization goal. We calculate the optimal rotation matrix and the translation vector using the dual quaternion registration algorithm, and get the helix parameters of the secondary structure which contain the number of residues per turn( ), helix radius( )and helix pitch( ). Furthermore, we can achieve the fitting of three-helix parameters of , , simultaneously with the dual quaternion registration, and can adjust the sliding windows to adapt to different error levels. Compared with the traditional helix fitting method, DQRFit has some advantages such as low computational complexity, strong anti-interference, and high fitting accuracy. It is proven that the precision of proposed DQRFit for α helix detection is comparable to that of the dictionary of secondary structure of proteins (DSSP), and is better than that of other traditional methods. This is of great significance for the protein structure classification and functional prediction, drug design, protein structure visualization and other fields in the future.

In this paper, a new method combining wavelet packet transform and multivariate multiscale entropy for the classification of epilepsy EEG signals is introduced. Firstly, the original EEG signals are decomposed at multi-scales with the wavelet packet transform, and the wavelet packet coefficients of the required frequency bands are extracted. Secondly, the wavelet packet coefficients are processed with multivariate multiscale entropy algorithm. Finally, the EEG data are classified by support vector machines (SVM). The experimental results on the international public Bonn epilepsy EEG dataset show that the proposed method can efficiently extract epileptic features and the accuracy of classification result is satisfactory.
Electroencephalography ; classification ; methods ; Entropy ; Epilepsy ; diagnosis ; physiopathology ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis

Diffusion tensor imaging technology can provide information on the white matter of the brain, which can be used to explore changes in brain tissue structure, but it lacks the specific description of the microstructure information of brain tissue. The neurite orientation dispersion and density imaging make up for its shortcomings. But in order to accurately estimate the brain microstructure, a large number of diffusion gradients are needed, and the calculation is complex and time-consuming through maximum likelihood fitting. Therefore, this paper proposes a kind of microstructure parameters estimation method based on the proximal gradient network, which further avoids the classic fitting paradigm. The method can accurately estimate the parameters while reducing the number of diffusion gradients, and achieve the purpose of imaging quality better than the neurite orientation dispersion and density imaging model and accelerated microstructure imaging via convex optimization model.
Brain/diagnostic imaging* ; Diffusion Magnetic Resonance Imaging ; Diffusion Tensor Imaging ; Neurites ; White Matter

OBJECTIVE: To explore the mediating effect of coping styles on aggressive behavior against bus drivers and their mental health status. METHODS: A total of 447 bus drivers were selected as the research objects using the method of judgment sampling. The aggressive behavior, mental health status and coping style of bus drivers were investigated using the Questionnaire of Aggressive Behaviors Against Bus Drivers, Symptom-Checklist 90 and Questionnaire of Simplified Coping Style Questionnaire. RESULTS: The median scores of aggressive behavior against bus drivers, positive coping style, negative coping style and mental health status were 50.0, 22.0, 10.0 and 125.0, respectively. Aggressive behavior against bus drivers and negative coping style were positively correlated with mental health status [Spearman correlation coefficient(r_S) were 0.27 and 0.42, respectively, P<0.01]. Positive coping style was not correlated with mental health status(r_S=-0.08, P>0.05). The total effect of aggressive behavior against bus drivers on their mental health status was 0.30. The mediating role of negative coping styles on aggressive behavior against bus drivers and their mental health status was 0.10, accounting for 33.1% of the total effect. CONCLUSION: Negative coping styles play a partial mediating role on the impact of aggressive behavior against bus drivers′ mental health status.

Aim To construct eukaryotic expressing plasmid of hi FGF2 ( high molecular weight isoform fi-broblast growth factor-2,hi FGF2) gene and to investi-gate its effect on apoptosis after its overexpression in HEK293 cells. Methods The DNA template primer was designed and synthesized. The pDsRed1-N1 plas-mids were digested by the restriction enzymes of Nhel and Hind III. The hi FGF2 was ligated with linearized pDsRed1-N1 by T4 DNA Ligase. The recombinant plasmid was identified by endonuclease digestion and sequenced. The recombinant hi FGF2 plasmid was transient transfected into HEK293 cells by Lipofectami-neTM 2000 Reagent. The transfection efficiency was de-tected by fluorescence inversion microscope. The cell apoptosis was detected by Annexin V-FITC/PI apopto-sis detection kit with flow cytometry analysis. Results The pDsRed1-N1 eukaryotic expression vector was consistent with the design. The recombinant hi FGF2 plasmid was transfected in HEK293 cells. The trans-fection rate was more than 70%. The FITC/PI dyeing rate in hi-FGF2 over-expression HEK297 cells was a-bout ( 29. 12 ± 2. 81 )%. Conclusions pDsRed1-N1 eukaryotic expression vector is successfully constructed and transfected into HEK293 cells. Over-expression of hi FGF2 induces cell apoptosis.

The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer's diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer's disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnosis* ; Neural Networks, Computer ; Machine Learning ; Brain ; Electroencephalography

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.
Humans ; Bayes Theorem ; Neural Networks, Computer ; Algorithms ; Brain ; Cognitive Dysfunction/diagnosis*