Perioperative use of dexmedetomidine is associated with reduction in postoperative analgesic requirements. This study examined whether dexmedetomidine added to i.v. patient-controlled analgesia (PCA) morphine could improve analgesia while reducing opioid-related side-effects. In this double-blinded, randomized, controlled study, 100 women undergoing abdominal total hysterectomy were allocated to receive either morphine 1 mg ml⁻¹ alone (Group M) or morphine 1 mg ml⁻¹ plus dexmedetomidine 5 µg ml⁻¹ (Group D) for postoperative i.v. PCA, which was programmed to deliver 1 ml per demand with a 5 min lockout interval and no background infusion. Cumulative PCA requirements, pain intensities, cardiovascular and respiratory variables, and PCA-related adverse events were recorded for 24 h after operation. Compared with Group M, patients in Group D required 29% less morphine during the 0–24 h postoperative period and reported significantly lower pain levels from the second postoperative hour onwards and throughout the study. Whereas levels of sedation were similar between the groups at each observational time point, decreases in heart rate and mean blood pressure from presurgery baseline at 1, 2, and 4 h after operation were significantly greater in Group D (by a range of 5–7 beats min⁻¹ and 10–13%, respectively). The 4–24 h incidence of nausea was significantly lower in Group D (34% vs 56.3%, P<0.05). There was no bradycardia, hypotension, oversedation, or respiratory depression. The addition of dexmedetomidine to i.v. PCA morphine resulted in superior analgesia, significant morphine sparing, less morphine-induced nausea, and was devoid of additional sedation and untoward haemodynamic changes.

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels. Furthermore, the Random Forest output was extensively linear in relation to BIS, thus with better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients’ anaesthetic level during surgeries.

One of the most challenging predictive data analysis efforts is accurate prediction of depth of anesthesia (DOA) indicators which has attracted a growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using deep convolutional neural network (CNN) for object recognition are becoming widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this paper, deep learning CNN with a range of different architectures, is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform (STFT) is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability. As a result, the best trained CNN model achieved an accuracy of 93.50%, interpreted as CNN’s deep learning to approximate the DOA by senior anesthesiologists, which highlights the potential of deep CNN combined with advanced visualization techniques for EEG-based brain mapping.

Citrate is an essential substrate for energy metabolism that plays critical roles in regulating cell growth and survival. However, the action of citrate in regulating metabolism, cognition, and aging at the organismal level remains poorly understood. Here, we report that dietary supplementation with citrate significantly reduces energy status and extends lifespan in Drosophila melanogaster. Our genetic studies in fruit flies implicate a molecular mechanism associated with AMP‐activated protein kinase (AMPK), target of rapamycin (TOR), and ketogenesis. Mice fed a high‐fat diet that supplemented with citrate or the ketone body β‐hydroxybutyrate (βOHB) also display improved metabolic health and memory. These results suggest that dietary citrate supplementation may prove to be a useful intervention in the future treatment of age‐related dysfunction.

EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal processing method to analyze EEG signals and compare them with existing BIS indexes from a commercial product (i.e., IntelliVue MP60 BIS module). Multivariate empirical mode decomposition (MEMD) algorithm is utilized to filter the EEG signals. A combination of two MEMD components (IMF2 + IMF3) is used to express the raw EEG. Then, sample entropy algorithm is used to calculate the complexity of the patients’ EEG signal. Furthermore, linear regression and artificial neural network (ANN) methods were used to model the sample entropy using BIS index as the gold standard. ANN can produce better target value than linear regression. The correlation coefficient is 0.790 ± 0.069 and MAE is 8.448 ± 1.887. In conclusion, the area under the receiver operating characteristic (ROC) curve (AUC) of sample entropy value using ANN and MEMD is 0.969 ± 0.028 while the AUC of sample entropy value without filter is 0.733 ± 0.123. It means the MEMD method can filter out noise of the brain waves, so that the sample entropy of EEG can be closely related to the depth of anesthesia. Therefore, the resulting index can be adopted as the reference for the physician, in order to reduce the risk of surgery.