Efficacy of transversus abdominis plane block on postoperative nausea and vomiting: a meta-analysis of randomized controlled trial.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a common postoperative complication, and Transversus abdominis plane (TAP) block can provide effective analgesia for surgical operation. However, but there is not enough evidence to prove its advantage for nausea and vomiting. The objective of this meta-analysis was to evaluate the efficacy of TAP block on PONV. METHODS: Two independent researchers conducted searches for randomized controlled trials (RCTs) in PubMed, Embase, and Cochrane Central Register of Controlled Trials. We used Review Manager software for meta-analysis. RESULTS: In this meta-analysis, twenty-six trials with 1981 patients were examined. The results showed that TAP block reduced postoperative nausea (Risk Difference (RD) = -0.10, 95% confidence interval (CI): -0.15 to -0.05) compared with no TAP block. TAP block reduced the dose of fentanyl (Standardized Mean Difference (SMD) = -1.17, 95% CI: -2.07 to -0.26) and morphine (SMD = -1.12, 95% CI: -2.10 to -0.13) compared with no TAP block, when the timing of administration was before surgery (RD = -0.13, 95% CI: -0.19 to -0.07). TAP block reduced postoperative nausea when the ropivacaine dosage is ≤ 100 mg (RD = -0.13, 95% CI: -0.21 to -0.06), bupivacaine dosage ≥ 100 mg ( RD = -0.08, 95% CI: -0.13 to -0.03), and when the ropivacaine concentration was ≤ 0.375% (RD = -0.11, 95% CI: -0.18 to -0.04). TAP block significantly reduced the incidence of nausea when the types of opioid drugs in PCA is tramadol (RD = -0.13, 95% CI: -0.24 to -0.03). TAP block could reduce the VAS (SMD= -0.99, 95% CI: -1.29 to -0.70) and reduce the time of extubation (SMD = -0.71, 95% CI: -1.34 to -0.08). CONCLUSION: The meta-analysis conducted in this study revealed that TAP block could reduce the incidence of PONV, and the efficacy of TAP block may be influenced by factors such as administration time, local anesthetic dosage and concentration, types of opioid drugs in PCA.

Efficacy of intravenous acetaminophen on postoperative shivering: A meta-analysis of randomized controlled trials.

PURPOSE: Postoperative shivering (POS) is a common and vital complication after anesthesia, which may result in serious consequences and uncomfortable experiences. Acetaminophen has been used to treat fever and mild to moderate pain. However, there is not enough evidence to prove its advantage for POS. This meta-analysis aimed to explore the prophylactic use of acetaminophen as a valid agent for POS. METHODS: Two researchers independently searched PubMed, the Cochrane Library, and Embase for controlled clinical trials. The meta-analysis of randomized controlled trials (RCTs) was performed by Review Manager. RESULTS: Nine trials with 856 patients were included in our meta-analysis. Acetaminophen significantly reduced POS compared with placebo (pooled risk ratio [RR]: 0.43, 95% confidence interval [CI]: 0.35-0.52). What is more, not only 15 mg/kg but also 1000 mg intravenous acetaminophen could reduce the incidence of shivering compared with placebo. CONCLUSION: Our present meta-analysis demonstrates that the intravenous prophylactic infusion of acetaminophen may prevent POS, and the results may provide new evidence to expand the clinical value of acetaminophen in addition to its routine usage.

Preparation of Thermochromic Vanadium Dioxide Films Assisted by Machine Learning.

In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization.