Comparison of anatomical and visual outcomes following different anti-vascular endothelial growth factor treatments in subretinal neovascular membrane secondary to type 2 proliferative macular telangiectasia

Graefe's Archive for Clinical and Experimental Ophthalmology - To evaluate central macular thickness (CMT), subfoveal choroidal thickness (SFCT), and visual outcomes following different...     

Helicobacter pylori infection in epileptic patients.

It is well known that there might be an epidemiological association between Helicobacter pylori infection and extraintestinal diseases. This study aimed at determining H. pylori infection in epileptic patients. Forty-seven cryptogenic epileptic patients (Group 1) and 35 healthy people (Group 2) participated in this study. Presence of H. pylori infection was examined by H. pylori stool antigen (HpSA), H. pylori IgG, and IgM. HpSA was detected in 21 participants (44.6%) in Group 1 and in 3 participants (8.5%) in Group 2. H. pylori IgM was positive in 27 participants (57.4%) in Group 1 and in 8 participants (22.8%) in Group 2. H. pylori IgG was positive in 37 participants (78.7%) in Group 1 and in 13 participants (38%) in Group 2. The difference of rates of HpSA, H. pylori IgM and IgG in Groups 1 and 2 were found statistically significant (chi2=4.18, p=0.04; chi2=9.18, p=0.0017; chi2=14.58, p<0.001, respectively). We also compared presence of H. pylori infection between the epileptic patients with poor and good prognosis; HpSA positivity was detected in 15 (62.5%) of 24 and 6 (26%) of 23, respectively, and the differences were statistically significant (chi2=6.30, p=0.012). H. pylori IgM positivity was detected in 16 (66%) of 24 patients with poor prognosis and 11 (47.8%) of 23 patients with good prognosis (p>0.05). H. pylori IgG positivity was detected in 18 (75%) of 24 patients with poor prognosis and 19 (82.6%) of 23 patients with good prognosis. The differences of H. pylori IgM and IgG positivity rates in epileptic patients with poor and good prognosis were not found statistically significant (p>0.05). These results suggest a probable association between the acute H. pylori infection and epilepsy, especially with poor prognosis.     

The effect of metoclopramide on gastric emptying in traumatic brain injury.

OBJECTIVE: Gastric paresis in traumatic brain injury (TBI) hinders the effectiveness of enteral support in this patient group. In this study we have investigated the effect of metoclopramide on gastric emptying in TBI patients. METHOD: In this prospective, randomized, controlled, double-blind study, 19 TBI patients with Glasgow Coma Scale scores of 3-11 were included. In all patients, enteral nutrition was commenced with a nasogastric feeding tube within 48 hours of trauma. Patients were randomized into two groups. In the metoclopramide (M) group, 10 mg metoclopramide was delivered intravenously three times daily for 5 days. In the control (C) group, an equal volume of saline was administered. Besides demographics, gastric emptying according to a paracetamol absorption test at days 0 and 5, time to reach target nutritional requirements, gastric residues, intolerance to feeding, nutritional complications, and clinical outcomes were recorded for each patient. RESULTS: The gastric residue rates were 2.7+/-7.4 mL and 8.1+/-17.7 mL per 100 patient days for groups C and M respectively (p=0.408). Similarly, feeding intolerance and complication rates did not significantly differ between groups C and M, (respectively p=0.543 and 0.930). Gastric emptying parameters also were similar between the study groups. CONCLUSION: We were unable to document any advantage to using metoclopramide in TBI patients. Simple intragastric enteral feeding with close monitoring of the possible complications seems to be sufficient with acceptable morbidity rates.     

Sympathetic skin responses from postauricular region in Meniere's disease.

OBJECTIVE: To investigate the sympathetic nervous system activity in Meniere's disease (MD) by recording sympathetic skin responses (SSRs) from the postauricular region (PA). METHODS: Twenty-one patients with definite unilateral MD diagnosis and 12 healthy volunteers were studied by evoking right and left PA-SSRs with electrical stimulation of the left median nerve at the wrist in attack and interval periods of MD. Mean latencies and maximum amplitudes were used in statistical analyses. RESULTS: In unilateral definite MD patients, the mean latencies were longer and the maximum amplitudes were smaller on the involved ear side than those on the normal ear side (p<0.01 for both amplitude and latency) and than those from the controls (p<0.01 and p<0.05). In three patients, there was no detectable PA-SSR on the involved ear side while there were SSRs on the healthy side. In four patients, the responses were absent bilaterally during the attack period. CONCLUSIONS: There is a marked asymmetric sympathetic hypofunction in the area of the PA region of the involved ear in MD patients. SIGNIFICANCE: The PA region is a new site for recording sympathetic skin responses. PA-SSR is a useful tool to investigate sympathetic nervous system function in MD patients.     

Application of classical and model-based spectral methods to ophthalmic arterial Doppler signals with uveitis disease

In this study, Doppler signals recorded from ophthalmic artery of 75 subjects were processed by PC-computer using classical and model-based methods. The classical method (fast Fourier transform) and three model-based methods (Burg autoregressive, moving average, least-squares modified Yule–Walker autoregressive moving average methods) were selected for processing ophthalmic arterial Doppler signals with uveitis disease. Doppler power spectra of ophthalmic arterial Doppler signals were obtained by using these spectrum analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of uveitis disease.     

A mixture of experts network structure for modelling Doppler ultrasound blood flow signals

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.     

An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks

In this paper, an intelligent system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective in detecting Doppler heart sounds. The correct classification rate was about 94% for abnormal and normal subjects.