Background and Purpose—: Intracerebral hemorrhage (ICH) is associated with high mortality and neurological deficits, and concurrent hyperglycemia usually worsens clinical outcomes. Aquaporin-4 (AQP-4) is important in cerebral water movement. Our aim was to investigate the role of AQP-4 in hyperglycemic ICH. Methods—: Hyperglycemia was induced by intraperitoneal injection of streptozotocin (STZ; 60 mg/kg) in adult Sprague–Dawley male rats. ICH was induced by stereotaxic infusion of collagenase/heparin into the right striatum. One set of rats was repeatedly monitored by MRI at 1, 4, and 7 days after ICH induction so as to acquire information on the formation of hematoma and edema. Another set of rats was killed and brains were examined for differences in the degree of hemorrhage and edema, water content, blood–brain barrier destruction, and AQP-4 expression. Results—: Hyperglycemia ICH rats exhibited increased brain water content, more severe blood–brain barrier destruction, and greater vasogenic edema as seen on diffusion-weighted MRI. Significant downregulation of AQP-4 was observed in STZ-treated rats after ICH as compared with non–STZ-treated rats. Apoptosis was greater on day 1 after ICH in STZ-treated rats. Conclusions—: The expression of AQP-4 in the brain is downregulated in hyperglycemic rats as compared with normoglycemic rats after ICH. This change is accompanied by increased vasogenic brain edema and more severe blood–brain barrier destruction.

GABAergic signaling in hippocampal pyramidal neurons undergoes a switch from depolarizing to hyperpolarizing during early neuronal development. Whether such a transformation of GABAergic action occurs in dentate granule cells (DGCs), located at the first stage of the hippocampal trisynaptic circuit, is unclear. Here, we use noninvasive extracellular recording to monitor the effect of synaptically released GABA on the DGC population. We find that GABAergic responses in adolescent and adult rat DGCs are still depolarizing from rest. Using a morphologically realistic DGC model, we show that GABAergic action, depending on its precise timing and location, can have either an excitatory or inhibitory role in signal processing in the dentate gyrus.

Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving. Accordingly, the assessment of the spirit status of the driver through the eyes blinking frequency and the measurement of physiological signals have emerged as effective methods. In this study, a drowsiness detection system is proposed to combine the detection of LF/HF ratio from heart rate variability (HRV) of photoplethysmographic imaging (PPGI) and percentage of eyelid closure over the pupil over time (PERCLOS), and to utilize the advantages of both methods to improve the accuracy and robustness of drowsiness detection. The proposed algorithm performs three functions, including LF/HF ratio from HRV status judgment, eye state detection, and drowsiness judgment. In addition, this study utilized a near-infrared webcam to obtain a facial image to achieve non-contact measurement, alleviate the inconvenience of using a contact wearable device, and for use in a dark environment. Furthermore, we selected the appropriate RGB channel under different light sources to obtain LF/HF ratio from HRV of PPGI. The main drowsiness judgment basis of the proposed drowsiness detection system is the use of algorithm to obtain sympathetic/parasympathetic nervous balance index and percentage of eyelid closure. In the experiment, there are 10 awake samples and 30 sleepy samples. The sensitivity is 88.9%, the specificity is 93.5%, the positive predictive value is 80%, and the system accuracy is 92.5%. In addition, an electroencephalography signal was used as a contrast to validate the reliability of the proposed method.

Drunk driving accidents have been rapidly increasing in recent times. Although the statistics show a decreasing trend in recent years, reports of drunk driving accidents are often seen in the news. To assess vehicle operators for drunk driving, the police still use breath-alcohol testers as the primary method. However, a certified instrument to measure alcohol consumption is expensive, and the mouthpiece used in the instrument is a consumable. Moreover, the breath detection method used involves contact measurement, which may cause hygiene concerns. To achieve more convenient and accurate detection, many researchers have proposed methods to replace the traditional breath-type measurement instruments. The present study proposes a two-stage neural network for recognition of drunk driving: the first stage uses the simplified VGG network to determine the age range of the subject, and the second stage uses the simplified Dense-Net to identify the facial features of drunk driving. The age discrimination stage obtained an accuracy of 86.36%. In addition, in drunk driving recognition tests among various age groups (18–30, 31–50, and ≥51 years), accuracies of 94%, 83%, and 81% were obtained, respectively. The overall system also showed a high accuracy of 89.62% and 87.44%, which proves the robustness of the system while supporting its practical application.