Role of ferroptosis in effects of anesthetics on multiple organ diseases: A literature review.

Anesthesiologists are often faced with patients combined with a series of organ injuries, such as acute lung injury, myocardial ischemia-reperfusion injury, and neurodegenerative diseases. With the in-depth study of these diseases, we are more aware of the choice and rational use of anesthetics for the prognosis of these patients. Ferroptosis is a new type of programmed cell death. This unique pattern of cell death, driven by an imbalance between oxides and antioxidants, is regulated by multiple cellular metabolic events, including redox homeostasis, iron handling, mitochondrial activity, and lipids peroxidation. Numerous studies confirmed that anesthetics modulate ferroptosis by interfering its machineries such as cystine-import-glutathione-glutathione peroxidase 4 axis, Heme oxygenase 1, nuclear factor erythroid 2-related factor 2, and iron homeostasis system. In this literature review, we systemically illustrated possible involvement of ferroptosis in effects of anesthetics and adjuvant drugs on multiple organ diseases, hoping our work may serve as a basis for further studies on regulating ferroptosis through anesthetics related pharmacological modulation and promoting the rational use of anesthetics.

A nomogram to predict postoperative nausea and vomiting in the ward following laparoscopic bariatric surgery.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a common and distressing complication of laparoscopic bariatric surgery (LBS). However, there is a lack of effective integrated prediction models for preventing and treating PONV in patients after LBS. METHODS: Based on a randomized controlled trial conducted between November 1, 2021, and May 13, 2022, we included 334 participants who underwent LBS according to the inclusion criteria. The database was divided randomly into training and validation cohorts in a ratio of 7:3. Least absolute shrinkage and selection operator plus multivariable logistic regression were used to identify independent predictors and construct a nomogram. The performance of the nomogram was assessed and validated by the area under the receiver operating characteristic curve (AUC), the concordance index (C-index), calibration plots, and a decision curve analysis (DCA). We also explored specific risk factors for PONV in patients with diabetes. RESULTS: The subjects were divided randomly into training (n = 234) and validation (n = 100) cohorts. Age, history of diabetes, type of surgery, and sugammadex use were incorporated to construct a nomogram prediction model. In the training cohort, the AUC and the optimism-corrected C-index were 0.850 [95% confidence interval (CI) 0.801-0.899] and 0.848, while in the validation cohort they were 0.847 (95% CI 0.768-0.925) and 0.844, respectively. The calibration plots showed good agreement between the predicted and actual observations. The DCA results demonstrated that the nomogram was clinically useful. The type of surgery, sugammadex use, and insulin level at 120 min were predictors of PONV in patients with diabetes with an AUC of 0.802 (95% CI 0.705-0.898). CONCLUSIONS: We developed and validated a prediction model for PONV in patients after LBS. A risk factor analysis of PONV in patients with diabetes provides clinicians with a more precise prophylactic protocol.

Penehyclidine hydrochloride for treating postoperative nausea and vomiting after laparoscopic bariatric surgery: a double-blinded randomized controlled trial.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a common and distressing complication of laparoscopic bariatric surgery (LBS). Penehyclidine hydrochloride has been reported to be effective in preventing PONV. Considering the potential preventive effects of penehyclidine against PONV, we hypothesized that intravenous infusion of penehyclidine may alleviate PONV within the first 48 h in patients scheduled for LBS. METHODS: Patients who underwent LBS were randomly assigned (1:2) to receive saline (Control group, n = 113) or a single intravenous dose of penehyclidine 0.5 mg (PHC group, n = 221). The primary outcome was incidence of PONV within the first 48 h postoperatively. Secondary endpoints included severity of PONV, need for rescue antiemetic therapy, volume of water intake, and time to first flatus. RESULTS: PONV occurred in 159 (48%) patients within the first 48 h postoperatively, including 51% in the Control group and 46% in the PHC group. There was no significant difference in the incidence or severity of PONV between the two groups (P > 0.05). Within the first 24 h and 24-48 h, no significant difference was found in incidence or severity of PONV, postoperative nausea, postoperative vomiting, need for rescue antiemetic therapy, or volume of water intake (P > 0.05). Kaplan-Meier curves showed that penehyclidine was significantly associated with a prolonged time to first flatus (median onset time: 22 h vs. 21 h, P = 0.036). CONCLUSIONS: Penehyclidine did not decrease incidence and severity of PONV in patients undergoing LBS. However, a single intravenous dose of penehyclidine (0.5 mg) was associated with a slightly prolonged time to first flatus. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR2100052418, http://www.chictr.org.cn/showprojen.aspx?proj=134893 , date of registration: 25/10/2021).

A reduced-form ensemble of short-term air quality forecasting with the Sparrow search algorithm and decomposition error correction.

The level of air pollution is reflected by the air quality index (AQI). People can use the AQI to organize their activities in a way that reduces or prevents exposure to air pollution altogether. Based on the AQI, governments, organizations, and businesses can also make plans to reduce air pollution. The multi-model ensemble has recently become a popular method for forecasting time series; however, it encounters the research problems of multi-parameter optimization and interaction analysis. To this end, a reduced-form ensemble of short-term air quality forecasting with the Sparrow search algorithm and decomposition error correction model is proposed in this paper. First, the data are decomposed using the CEEMDAN decomposition algorithm. Second, the Sparrow search algorithm is used in the model training process to obtain the optimal hyperparameters of the deep learning model and construct the optimal deep learning model. Next, the constructed models are used to predict the decomposed data, and the Lagrange multiplier method is used to determine the weights of each deep learning model. At last, the prediction results of each deep learning model are combined according to the weights to obtain the combined prediction results. Experiments show that (1) GRU, Bi-GRU, LSTM, and Bi-LSTM are used to predict the undecomposed data and the data decomposed by CEEMADN. The outcomes demonstrate that the CEEMDAN decomposition technique can enhance the accuracy of the forecast, specifically an 11.248% reduction in average RMSE and a 0.865% increase in average R2. (2) A multi-model combination method based on the Lagrange multiplier method is designed, which can obtain the weights of each deep learning model, and the weights can combine multiple models. The results of the multi-model combination are better than those of the single model. (3) The Lagrange multiplier method was compared with the simple average combination model and the MAE inverse combination model. The experimental results show that the results obtained using the Lagrange multiplier method are better than the other two.

Lattice Boltzmann simulation for phase separation with chemical reaction controlled by thermal diffusion.

This study investigates phase separation behavior and pattern formation in a binary fluid with chemical reaction controlled by thermal diffusion. By incorporating the Arrhenius equation into the lattice Boltzmann method (LBM), the coupling effects of the pre-exponential factor K, viscosity [Formula: see text], and thermal diffusion D on phase separation were successfully evaluated. The effect of the competition between thermal diffusion and concentration on the phase separation morphology and dynamics of binary mixtures under a chemically reacting controlled by slow cooling is assessed based on the extended LBM. The calculations indicated that increases in viscosity and thermal diffusion can obtain interconnected structures (ISs) and lamellar structures (LSs) for cases with small K. However, concentric phase-separated structures (CSs) were observed in cases with large K. The increase in the degree and efficiency of phase separation were significantly greater in cases with decreased viscosity and increased thermal diffusion.

Daily air quality index forecasting with hybrid models: A case in China.

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management.