Preoperative Phenacetin Metabolism Test in the Prediction of Postoperative Liver Dysfunction of Patients with Hepatocellular Carcinoma.

BACKGROUND The risk of postoperative liver dysfunction (PLD) in patients with injured livers, such as in hepatocellular carcinoma (HCC), is still not negligible. Phenacetin metabolism test can reflect hepatic functional reserve in patients with chronic hepatic damage. The aim of this study was to assess the ability of phenacetin metabolism test to predict PLD in patients with HCC receiving partial hepatectomy. MATERIAL AND METHODS Forty-nine patients with HCC undergoing partial hepatectomy between 2014 and 2016 were included at Huashan Hospital, Fudan University. The phenacetin metabolism test was used to assess the hepatic functional reserve. The ratio of total plasma paracetamol to phenacetin was collected in patients at 2 h after oral administration of 1.0 g phenacetin, recorded 5 days prior to surgery and on the fifth postoperative day. Phenacetin metabolism test, Child-Pugh classification, and Model for End-Stage Liver Disease (MELD) score were correlated with PLD. RESULTS Of 49 patients with HCC, 13 patients (26.5%) had PLD. The association between the ratio of total plasma paracetamol to phenacetin and PLD was statistically significant (p=0.0061) and the correlation coefficient was -0.647 (p=0.0082). The phenacetin metabolism test showed a larger area under the receiver operating characteristic (ROC) curve value (0.735) than Child-Pugh's classification (0.472) and MELD score (0.419). Using the calculated cutoff of 0.6, the lower ratio of total plasma paracetamol to phenacetin preoperatively was chosen to specifically identify patients with PLD. The sensitivity and specificity were 0.657 and 0.892, respectively. CONCLUSIONS Phenacetin metabolism test could be preoperatively used in predicting PLD in HCC patients receiving partial hepatectomy. It potentially provides better prediction than Child-Pugh classification and MELD score.

Emotion recognition using Kinect motion capture data of human gaits.

Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker's emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants' gaits under neutral state, induced anger and induced happiness were recorded by two Kinect cameras, and the original data were processed through joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation. Features of gait patterns were extracted from 3-dimentional coordinates of 14 main body joints by Fourier transformation and Principal Component Analysis (PCA). The classifiers NaiveBayes, RandomForests, LibSVM and SMO (Sequential Minimal Optimization) were trained and evaluated, and the accuracy of recognizing anger and happiness from neutral state achieved 80.5% and 75.4%. Although the results of distinguishing angry and happiness states were not ideal in current study, it showed the feasibility of automatically recognizing emotional states from gaits, with the characteristics meeting the application requirements.

Emotion recognition based on customized smart bracelet with built-in accelerometer.

BACKGROUND: Recently, emotion recognition has become a hot topic in human-computer interaction. If computers could understand human emotions, they could interact better with their users. This paper proposes a novel method to recognize human emotions (neutral, happy, and angry) using a smart bracelet with built-in accelerometer. METHODS: In this study, a total of 123 participants were instructed to wear a customized smart bracelet with built-in accelerometer that can track and record their movements. Firstly, participants walked two minutes as normal, which served as walking behaviors in a neutral emotion condition. Participants then watched emotional film clips to elicit emotions (happy and angry). The time interval between watching two clips was more than four hours. After watching film clips, they walked for one minute, which served as walking behaviors in a happy or angry emotion condition. We collected raw data from the bracelet and extracted a few features from raw data. Based on these features, we built classification models for classifying three types of emotions (neutral, happy, and angry). RESULTS AND DISCUSSION: For two-category classification, the classification accuracy can reach 91.3% (neutral vs. angry), 88.5% (neutral vs. happy), and 88.5% (happy vs. angry), respectively; while, for the differentiation among three types of emotions (neutral, happy, and angry), the accuracy can reach 81.2%. CONCLUSIONS: Using wearable devices, we found it is possible to recognize human emotions (neutral, happy, and angry) with fair accuracy. Results of this study may be useful to improve the performance of human-computer interaction.

[Effectiveness and safety of laparoscopic presacral neurectomy in treating endometriosis-associated pain].

OBJECTIVE: To explore the effectiveness and safety of laparoscopic presacral neurectomy (LPN) in treating endometriosis-associated pain. METHODS: Totally 64 patients with endometriosis were divided into two groups using prospective non-random method. Patients in the control group received only the conventional laparoscopic resection of endometriosis lesions, while patients in the LPN group underwent LPN in addition to the resection of endometriosis lesions. The pre-operative pain scores, intra-operative staging results, surgical duration, intra-surgical blood loss, post-operative pain relief were compared between these two groups. RESULTS: These two groups showed no significant differences in terms of age, body weight, pre-operative pain score, surgery staging, surgical duration, and intra-operative blood loss (all P > 0.05). All patients were followed up for 6 to 18 months (median: 12.8 months). The post-operative pain relief rate was 89.28% (25/ 28) in LPN group and 61.29% (19/31) in the control group (P = 0.030). CONCLUSION: LPN can effectively and safely in treating endometriosis and its associated pain.