Interactions of propofol and remifentanil on bispectral index under 66% N(2)O: analysis by dose-effect curve, isobologram, and combination index

Logistic Regression Is Non-Inferior to the Response Surface Model in Patient Response Prediction of Video-Assisted Thoracoscopic Surgery

Response surface models (RSMs) are a new trend in modern anesthesia. RSMs have demonstrated significant applicability in the field of anesthesia. However, the comparative analysis between RSMs and logistic regression (LR) in different surgeries remains relatively limited in the current literature. We hypothesized that using a total intravenous anesthesia (TIVA) technique with the response surface model (RSM) and logistic regression (LR) would predict the emergence from anesthesia in patients undergoing video-assisted thoracotomy surgery (VATS). This study aimed to prove that LR, like the RSM, can be used to improve patient safety and achieve enhanced recovery after surgery (ERAS). This was a prospective, observational study with data reanalysis. Twenty-nine patients (American Society of Anesthesiologists (ASA) class II and III) who underwent VATS for elective pulmonary or mediastinal surgery under TIVA were enrolled. We monitored the emergence from anesthesia, and the precise time point of regained response (RR) was noted. The influence of varying concentrations was examined and incorporated into both the RSM and LR. The receiver operating characteristic (ROC) curve area for Greco and LR models was 0.979 (confidence interval: 0.987 to 0.990) and 0.989 (confidence interval: 0.989 to 0.990), respectively. The two models had no significant differences in predicting the probability of regaining response. In conclusion, the LR model was effective and can be applied to patients undergoing VATS or other procedures of similar modalities. Furthermore, the RSM is significantly more sophisticated and has an accuracy similar to that of the LR model; however, the LR model is more accessible. Therefore, the LR model is a simpler tool for predicting arousal in patients undergoing VATS under TIVA with Remifentanil and Propofol.

Thermodynamic Interpretation of a Machine-Learning-Based Response Surface Model and Its Application to Pharmacodynamic Synergy between Propofol and Opioids

Propofol and fentanyl are commonly used agents for the induction of anesthesia, and are often associated with hemodynamic disturbances. Understanding pharmacodynamic impacts is vital for parasympathetic and sympathetic tones during the anesthesia induction period. Inspired by the thermodynamic interaction between drug concentrations and effects, we established a machine-learning-based response surface model (MLRSM) to address this predicament. Then, we investigated and modeled the biomedical phenomena in the autonomic nervous system. Our study prospectively enrolled 60 patients, and the participants were assigned to two groups randomly and equally. Group 1 received propofol first, followed by fentanyl, and the drug sequence followed an inverse procedure in Group 2. Then, we extracted and analyzed the spectrograms of electrocardiography (ECG) and pulse photoplethysmography (PPG) signals after induction of propofol and fentanyl. Eventually, we utilized the proposed MLRSM to evaluate the relationship between anesthetics and the integrity/balance of sympathetic and parasympathetic activity by employing the power of high-frequency (HF) and low-frequency (LF) bands and PPG amplitude (PPGA). It is worth emphasizing that the proposed MLRSM exhibits a similar mathematical form to the conventional Greco model, but with better computational performance. Furthermore, the MLRSM has a theoretical foundation and flexibility for arbitrary numbers of drug combinations. The modeling results are consistent with the previous literature. We employed the bootstrap algorithm to inspect the results’ consistency and measure the various statistical fluctuations. Then, the comparison between the modeling and the bootstrapping results was used to validate the statistical stability and the feasibility of the proposed MLRSM.

Synergism and mechanism of Astragaloside IV combined with Ginsenoside Rg1 against autophagic injury of PC12 cells induced by oxygen glucose deprivation/reoxygenation

The aim of this study was to explore the effect by which the combination of Astragaloside IV (AST IV) and Ginsenoside Rg1 (Rg1) resisted autophagic injury in PC12 cells induced by oxygen glucose deprivation/reoxygenation (OGD/R). We studied the nature of the interaction between AST IV and Rg1 that inhibited autophagy through the Isobologram method, and investigated the synergistic mechanism via the PI3K I/Akt/mTOR and PI3K III/Becline-1/Bcl-2 signaling pathways. Our results showed that, based on the 50% inhibiting concentration (IC50), AST IV combined with Rg1 at a 1:1 ratio resulted in a synergistic effect, whereas the combination of the two had an antagonistic effect on autophagy at ratios of 1:2 and 2:1. Meanwhile, AST IV and Rg1 alone increased cell survival and decreased lactate dehydrogenase (LDH) leakage induced by OGD/R, reduced autophagosomes and the LC3 II positive patch, down-regulated the LC3 II/LC3 I ratio and up-regulated the p62 protein; the 1:1 combination enhanced these effects. Mechanistic study showed that Rg1 and the 1:1 combination increased the phosphorylation of PI3K I, Akt and mTOR; the effects of the combination were greater than those of the drugs alone. AST IV and the 1:1 combination suppressed the expression of PI3K III and Becline-1, and the combination elevated Bcl-2 protein expression; the effects of the combination were better than those of the drugs alone. These results suggest that after 2 h-OGD followed by reoxygenation for 24h, PC12 cells suffer excessive autophagy and damage, which are blocked by AST IV or Rg1; moreover, the combination of AST IV and Rg1 at a 1:1 ratio of their IC50 concentrations has a synergistic inhibition on autophagic injury. The synergistic mechanism may be associated with the PI3K I/Akt/mTOR and PI3K III/Becline-1/Bcl-2 signaling pathways.