A comparative study of surgical outcomes using a Colorado microdissection needle versus standard-size needle electrocautery in one-stage hypospadias repair using a transverse preputial island flap.

BACKGROUND: The purpose of this study was to compare the surgical outcomes of a Colorado microdissection needle (CMN) with that of a standard-size electrocautery needle in one-stage hypospadias repair using a transverse preputial island flap (TPIF). METHODS: The records of patients who received hypospadias repair from September 2012 to October 2013 were retrospectively reviewed. Patients were divided into a group that received repair using a CMN and those in which a standard-size electrocautery needle was used. Data collected and compared included age, types of hypospadias, duration of surgery, intraoperative blood loss, and postoperative edema and complications. RESULTS: There were 51 patients in the CMN group and 44 in the standard needle group, and the groups were similar with respect to age and type of hypospadias. The median surgery time for the CMN group was significantly shorter than that of the standard group (15.7 minutes vs. 20.6 minutes, respectively, P<0.001). At postoperative day 7 and day 30, the CMN group had significantly less patients with edema than the standard needle group (31.4% vs. 65.9%, P<0.01; and 37.3% vs. 79.5%, P<0.001, respectively). The overall complication rate has no significant difference between two groups. CONCLUSIONS: The use of CMN for tissue dissection and separation in hypospadias repair can facilitate foreskin degloving, shape the flap in a more efficient way, and help maintain adequate blood supply for the new urethra and its skin coverage.

Coding Properties of Three Intrinsically Distinct Retinal Ganglion Cells under Periodic Stimuli: A Computational Study.

As the sole output neurons in the retina, ganglion cells play significant roles in transforming visual information into spike trains, and then transmitting them to the higher visual centers. However, coding strategies that retinal ganglion cells (RGCs) adopt to accomplish these processes are not completely clear yet. To clarify these issues, we investigate the coding properties of three types of RGCs (repetitive spiking, tonic firing, and phasic firing) by two different measures (spike-rate and spike-latency). Model results show that for periodic stimuli, repetitive spiking RGC and tonic RGC exhibit similar spike-rate patterns. Their spike- rates decrease gradually with increased stimulus frequency, moreover, variation of stimulus amplitude would change the two RGCs' spike-rate patterns. For phasic RGC, it activates strongly at medium levels of frequency when the stimulus amplitude is low. While if high stimulus amplitude is applied, phasic RGC switches to respond strongly at low frequencies. These results suggest that stimulus amplitude is a prominent factor in regulating RGCs in encoding periodic signals. Similar conclusions can be drawn when analyzes spike-latency patterns of the three RGCs. More importantly, the above phenomena can be accurately reproduced by Hodgkin's three classes of neurons, indicating that RGCs can perform the typical three classes of firing dynamics, depending on the distinctions of ion channel densities. Consequently, model results from the three RGCs may be not specific, but can also applicable to neurons in other brain regions which exhibit part(s) or all of the Hodgkin's three excitabilities.

Adaptation-dependent synchronization transitions and burst generations in electrically coupled neural networks.

A typical feature of neurons is their ability to encode neural information dynamically through spike frequency adaptation (SFA). Previous studies of SFA on neuronal synchronization were mainly concentrated on the correlated firing between neuron pairs, while the synchronization of neuron populations in the presence of SFA is still unclear. In this study, the influence of SFA on the population synchronization of neurons was numerically explored in electrically coupled networks, with regular, small-world, and random connectivity, respectively. The simulation results indicate that cross-correlation indices decrease significantly when the neurons have adaptation compared with those of nonadapting neurons, similar to previous experimental observations. However, the synchronous activity of population neurons exhibits a rather complex adaptation-dependent manner. Specifically, synchronization strength of neuron populations changes nonmonotonically, depending on the degree of adaptation. In addition, single neurons in the networks can switch from regular spiking to bursting with the increase of adaptation degree. Furthermore, the connection probability among neurons exhibits significant influence on the population synchronous activity, but has little effect on the burst generation of single neurons. Accordingly, the results may suggest that synchronous activity and burst firing of population neurons are both adaptation-dependent.

Ionic mechanisms underlying tonic and phasic firing behaviors in retinal ganglion cells: a model study.

In the retina, the firing behaviors that ganglion cells exhibit when exposed to light stimuli are very important due to the significant roles they play in encoding the visual information. However, the detailed mechanisms, especially the intrinsic properties that generate and modulate these firing behaviors is not completely clear yet. In this study, 2 typical firing behaviors­i.e., tonic and phasic activities, which are widely observed in retinal ganglion cells (RGCs)­are investigated. A modified computational model was developed to explore the possible ionic mechanisms that underlie the generation of these 2 firing patterns. Computational results indicate that the generation of tonic and phasic activities may be attributed to the collective actions of 2 kinds of adaptation currents, i.e., an inactivating sodium current and a delayed-rectifier potassium current. The concentration of magnesium ions has crucial but differential effects in the modulation of tonic and phasic firings, when the model neuron is driven by N-methyl-D-aspartate (NMDA) -type synaptic input instead of constant current injections. The proposed model has robust features that account for the ionic mechanisms underlying the tonic and phasic firing behaviors, and it may also be used as a good candidate for modeling some other firing patterns in RGCs.

Early event-related potential components in face perception reflect the sequential neural activities.

Event-related potential (ERP) studies report that early components P120, N170 and VPP are associated with face processing. Several lines of evidence suggest that VPP is the positive counterpart of N170, and they are generated by the same brain sources. However, whether P120 has a negative counterpart and the relations among these early components (i.e. P120, N170, VPP) remain unclear. In this study, the scalp electroencephalogram was recorded when the subjects passively viewed different stimuli, and ERP was calculated. The synchronization of electroencephalography signals between fronto-central and bilateral occipitotemporal sites was evaluated, and independent component analysis was employed to seek the face-sensitive independent components and their corresponding sources. We found that P120 had the negative counterpart, i.e., VN120; moreover, VN120-VPP and P120-N170 complexes were generated by the same sources located in fusiform gyrus, which reflected the same sequential neural activities of face processing.

Use of ANN and Complexity Measures in Cognitive EEG Discrimination.

The purpose of this paper is to apply BP ANN to the discrimination of three kinds of subjects (clinical diagnosed 62 schizophrenic patients, 48 depressive patients and 26 normal controls) respectively in resting state with eyes closed and three cognitive tasks, with EEG complexity measures used as feature vectors. EEG activity is recorded from 16 scalp electrodes and recordings are digitized for off-line processing. Features vectors based on Lep-Ziv complexity and classification with ANN are implemented in Matlab6.5. The comparison between the results of classifying in four states is illustrated and discussed. The classification accuracies achieved are 60% and over. The results show that ANN is an effective approach for discrimination of these three kinds of objects both in baseline and some cognitive states.