This study was undertaken to investigate the relationship between cerebrospinal fluid abnormalities and prognosis in pediatric refractory purulent meningitis. Ninety cases of pediatric refractory purulent meningitis were stratified into "good" (n=33) or "poor" (n=57) prognosis groups according to the Glasgow clinical outcome scores. The symptoms, laboratory results, and prognosis were compared by using univariate and multivariate logistic regression analyses. Univariate analysis showed that poor prognosis was associated with: unequal pupil size in both eyes; positive Babinski sign; CSF-WBC >500×10/L, CSF protein concentration >1.0g/L, CSF glucose content <1.5mmol/L; initial procalcitonin result >0.1ng/dL on admission; hemoglobin <90g/L during hospitalization; abnormal head imaging, and abnormal electroencephalogram. On multivariate analysis only unequal pupil size in both eyes and CSF glucose content <1.5mmol/L remained significant. The CSF protein concentration was significantly different between groups at discharge. The cutoff value was 0.68g/L. We recommend that discharged patients meet the following criteria: full antibiotic course and over 1 week of defervesce, disappearance of acute phase symptoms, CSF-WBC ≤28×10/L, CSF glucose >1.75mmol/L, and protein <0.68g/L. The patient may be discharged for follow-up if no relapse occurs during 3-5 days of observation after drug withdrawal.

&lt;p&gt;Earthquakes are reported to relate to rupture phenomena in complex self-organizing systems. Hence, the earthquake rupture is regarded as a critical point. The preparation process of an earthquake could be considered as the crustal system approaching this critical point. Complex dynamical systems can have critical tipping points at which a sudden shift to a contrasting dynamical regime may occur; in the meantime, the time series of the systems can behave much differently. Although it is extremely challenging to predict such critical points before they are reached, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate a wide class of systems if a critical threshold is approaching. Those precursory signals include increasing correlations and variance, varying skewness, and so on. The critical transition of a system includes spatial criticality and temporal criticality. In this study, we attempt to research the spatial and temporal criticality of the crustal system by using the self-potential (SP) signals of the Taiwan Geoelectric Monitoring System (GEMS). The GEMS network consists of 20 SP stations with an interstation distance of 50 km. We calculate the correlations of the daily signals between any two stations, which formed an adjacency matrix. Then, we estimate the connectivity density based on the adjacency matrix and compare the daily connectivity density time series with &lt;em&gt;M&lt;sub&gt;L&lt;/sub&gt;&lt;/em&gt; &amp;#8805; 5 earthquakes. We would expect to find out high connectivity densities before a strong earthquake. This would mean that earthquake-related telluric currents flow out through the GEMS stations during the earthquake preparation process; hence, the SP signals of most stations would almost be connected. As a result, we might establish an earthquake forecasting technique using the SP data based on the concept of the critical-point theory.&lt;/p&gt;

Abstract. Geoelectric time series (TS) have long been studied for their potential for probabilistic earthquake forecasting, and a recent model (GEMSTIP) directly used the skewness and kurtosis of geoelectric TS to provide times of increased probability (TIPs) for earthquakes for several months in the future. We followed up on this work by applying the hidden Markov model (HMM) to the correlation, variance, skewness, and kurtosis TSs to identify two hidden states (HSs) with different distributions of these statistical indexes. More importantly, we tested whether these HSs could separate time periods into times of higher/lower earthquake probabilities. Using 0.5 Hz geoelectric TS data from 20 stations across Taiwan over 7 years, we first computed the statistical index TSs and then applied the Baum–Welch algorithm with multiple random initializations to obtain a well-converged HMM and its HS TS for each station. We then divided the map of Taiwan into a 16-by-16 grid map and quantified the forecasting skill, i.e., how well the HS TS could separate times of higher/lower earthquake probabilities in each cell in terms of a discrimination power measure that we defined. Next, we compare the discrimination power of empirical HS TSs against those of 400 simulated HS TSs and then organized the statistical significance values from this cellular-level hypothesis testing of the forecasting skill obtained into grid maps of discrimination reliability. Having found such significance values to be high for many grid cells for all stations, we proceeded with a statistical hypothesis test of the forecasting skill at the global level to find high statistical significance across large parts of the hyperparameter spaces of most stations. We therefore concluded that geoelectric TSs indeed contain earthquake-related information and the HMM approach is capable of extracting this information for earthquake forecasting.