1.Application of ultrasound-guided ilioinguinal/iliohypogastric nerve blocks marked by arteriae circumflexa ilium profundain elderly hernia surgery
Jianfeng PU ; Meifang WANG ; Silei PAN ; Zhiqiang SHEN ; Wanlin FENG
The Journal of Clinical Anesthesiology 2017;33(10):974-976
Objective To explore the clinical effect of ultrasound-guided ilioinguinal/iliohypo-gastric nerve blocks marked by arteriae circumflexa ilium profunda in elderly hernia surgery. Methods Forty ASA Ⅰ-Ⅲ grade patients (33 males and 7 females)of 65-90 years old scheduled for elective hernia surgery were randomly divided into two groups (n =20).In group T,patients received ilioinguinal/iliohypogastric nerve blocks bytraditional anatomical positioning;in group V,patients re-ceived ultrasound-guided ilioinguinal/iliohypogastric nerve blocks marked by arteriae circumflexa ilium profunda.The comparison was made between the two groups in term of onset time of anesthe-sia,VAS score of intraoperative and postoperative 6 h.Anesthesia satisfaction,incidence of uros-chesis,misplacement local anesthetics into blood-vessels were recorded.Results The onset time of anesthesia in group V was significantly shorter than that in group T [(6.1 ± 1.8)min vs (12.1 ± 2.0)min,P <0.05].The VAS score of intraoperative in group T was significantly higher than that of group V [(4.5 ± 1.1 )scores vs (2.1 ± 0.9 )scores,P < 0.05 ].The anesthesia satisfaction of group V was higher than that of group T (P <0.05).There was one misplacement local anesthetics into blood-vessels in group T.Conclusion Ultrasound-guided ilioinguinal/iliohypogastric nerve blocks marked by arteriae circumflexa ilium profunda can provide safe,effective and reliable anesthesia in elderly hernia surgery.
2.Mouse liver proteome database.
Yang LIU ; Jinwen FENG ; Wanlin LIU ; Jun QIN ; Chen DING ; Fuchu HE
Chinese Journal of Biotechnology 2019;35(9):1715-1722
The liver is the metabolic center of mammalian body. Systematic study on liver's proteome expression under different physiological and pathological conditions helps us understand the functional mechanisms of the liver. With the rapid development of liquid chromatography tandem mass spectrometry technique, numerous studies on liver physiology and pathology features produced a large number of proteomics data. In this paper, 834 proteomics experiments of mouse liver were systematically collected and the mouse liver proteome database (Mouse Liver Portal, http://mouseliver.com) was established. The Mouse Liver Portal contains the liver's proteomics data under different physiology and pathology conditions, such as different gender, age, circadian rhythm, cell type and different phase of partial hepatectomy, non-alcoholic fatty liver. This portal provides the changes in proteins' expression in different conditions of the liver, differently expressed proteins and the biological processes which they are involved in, potential signal transduction and regulatory networks. As the most comprehensive mouse liver proteome database, it can provide important resources and clues for liver biology research.
Animals
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Chromatography, Liquid
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Databases, Factual
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Liver
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Mice
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Proteome
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Proteomics
3.Emotion Recognition Based on Multiple Physiological Signals.
Shali CHEN ; Liuyi ZHANG ; Feng JIANG ; Wanlin CHEN ; Jiajun MIAO ; Hang CHEN
Chinese Journal of Medical Instrumentation 2020;44(4):283-287
Emotion is a series of reactions triggered by a specific object or situation that affects a person's physiological state and can, therefore, be identified by physiological signals. This paper proposes an emotion recognition model. Extracted the features of physiological signals such as photoplethysmography, galvanic skin response, respiration amplitude, and skin temperature. The SVM-RFE-CBR(Recursive Feature Elimination-Correlation Bias Reduction-Support Vector Machine) algorithm was performed to select features and support vector machines for classification. Finally, the model was implemented on the DEAP dataset for an emotion recognition experiment. In the rating scale of valence, arousal, and dominance, the accuracy rates of 73.5%, 81.3%, and 76.1% were obtained respectively. The result shows that emotional recognition can be effectively performed by combining a variety of physiological signals.
Arousal
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Emotions
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Galvanic Skin Response
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Humans
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Photoplethysmography
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Support Vector Machine