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*

This article was aimed to study the relevance between traditional Chinese medicine ( TCM ) syndromes and the treatment of chemotherapy and targeted therapy , in order to provide theoretical support for TCM syndromes of lung cancer in the clinical application . Uniform TCM syndromes questionnaire was used in the TCM syndrome differentiation among lung cancer patients from the Oncology Department of Longhua Hospital, Shanghai University of Traditional Chinese Medicine and TCM Department of Shanghai Pulmonary Hospital. The analysis was made on correlation between distribution rules of TCM syndromes and chemotherapy and targeted therapy . The results showed that among primary lung cancer patients , the most TCM syndromes were syndrome of lung-yin deficiency , lung-q i deficiency , blood stasis in lung collaterals , spleen-q i deficiency , lung-yin deficiency with fire excess . There were certain correlation in TCM syndromes and different TCM therapeutic methods , in which targeted therapy had great significant effect on syndrome of lung-yin deficiency ( P < 0 . 05 ) , after targeted therapy syndrome of lung-yin deficiency increased obviously . It was concluded that there were some regulations in TCM syndromes of lung cancer which take syndrome of lung-yin deficiency, lung-qi deficiency, blood stasis in lung collaterals, spleen-qi deficiency, lung-yin deficiency with fire excess. There were certain correlation in TCM syndromes and different TCM therapeutic methods , after targeted therapy syndrome of lung-yin deficiency increased obviously .

0 05). Positive expression rates of Fas and bcl-2 were 60 0% and 92 9% respectively and Fas increased along with the escalation of the pathological grading (?=0 584,P0 05). Conclusions DNA ploidy aberration,Fas and bcl-2 overexpression could play an important role in the development of esophageal carcinoma .