General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Background Suicidal ideation serves as a sensitive predictive indicator for non-suicidal self-injury(NSSI)behavior,and NSSI behavior brings profound effects to both mental and physical health of senior high school students.Existing studies have made exploration on the relationship among insomnia,anxiety and NSSI behavior in the general population,but the internal mechanism of NSSI behavior in senior high school students with suicidal ideation requires further study.Objective To explore the pathway of anxiety between insomnia and NSSI behavior in senior high school students,so as to provide references for the intervention on NSSI behavior in such demographic.Methods From December 10 to 15,2021,cluster sampling was adopted to select 2 572 senior high school students across three senior high schools in Hubei Province as the research objects.Assessment was conducted by using Insomnia Severity Index(ISI),Generalized Anxiety Disorder Scale-7 item(GAD-7)and Adolescents Self-Harm Scale.Spearman correlation analysis was conducted to examine the correlation among scores of scales finished by senior high school students with suicidal ideation.Model 4 from the SPSS 20.0 Process 4.2 was employed to test the mediating path of anxiety between insomnia and NSSI behavior.Results A total of 2 421 valid questionnaires(94.13%)were recovered,which revealed 554 cases(22.88%)with suicidal ideation and 408 cases(16.85%)with NSSI behavior.The scores of ISI,GAD-7 and Adolescents Self-Harm Scale of senior high school students with suicidal ideation were higher than those of students without suicidal ideation(Z=17.124,20.611,21.314,P<0.01).As correlation analysis showed,the ISI score of senior high school students with suicidal ideation was positively correlated with scores of GAD-7 and Adolescents Self-Harm Scale(r=0.646,0.354,P<0.01),and GAD-7 score was positively correlated with the score of Adolescents Self-Harm Scale(r=0.375,P<0.01).Among senior high school students with suicide ideation,anxiety acted in a mediating role between insomnia and NSSI behavior,with effect value of 0.111(95%CI:0.053～0.185),accounting for 53.88%of the total effect.Conclusion Insomnia of senior high school students with suicidal ideation can not only directly affect NSSI behavior,but also indirectly affect NSSI behavior through anxiety.

OBJECTIVE:To establish the quality standard of Baoshenling capsules.METHODS:Radix Astragali,Radix et Rhizoma Rhei,Ligusticum chuanxiong and Hirudo were identified qualitatively by TLC,and the content of diphenyl ethylene in Baoshenling capsules was determined by HPLC.RESULTS:The characteristic TLC spots of Radix Astragali,Radix et Rhizoma Rhei,Ligusticum chuanxiong and Hirudo were identified accurately.The linear range of diphenyl ethylene was 0.176～0.880?g(r = 0.999 8) with an average recovery rate of 98.9%(RSD = 1.3%,n =6).CONCLUSION:The established standard is applicable for the quality control of Baoshenling capsules.