Purpose Postoperative acute kidney injury (AKI) is a frequent complication in elderly patients that increases morbidity and mortality. Approximately 1.7 million people die from AKI worldwide every year. Dexmedetomidine (Dex) is often used as an adjunct to multimodal analgesia. Our study investigated whether Dex could safely decrease the incidence of AKI in elderly patients undergoing major joint replacement. Methods A single-center retrospective study was conducted in patients aged >65 years undergoing major joint replacement. Propensity score–matching analysis was used, and a total of 1,006 patients were matched successfully. The primary outcome was the incidence of postoperative AKI. Secondary outcomes included perioperative adverse complications, opioid consumption, time to extubation, and length of hospital stay. Results Among the 1,006 patients included, postoperative AKI occurred in 9.3% (n=94). The Dex group (perioperative Dex infusion) had lower incidence of postoperative AKI than the control group (7.2% vs 11.5%, P =0.017). Compared with the control group, the Dex group had less opioid consumption ( P <0.05), reduced time to extubation ( P =0.004), and shorter length of hospital stay ( P =0.001). The Dex group also showed higher incidence of bradycardia (20.1% vs 15.1%, P =0.038). There were no differences in intraoperative hypotension (19.5% vs 17.5%), postoperative nausea and vomiting (4.2% vs 5.4%), time in PACU (45.0±6.4 vs 45.5±6.2 minutes), or rate of ICU admission (9.7% vs 11.1%) between the Dex group and control group (All P >0.05). Conclusion This retrospective study showed Dex infusion in elderly patients undergoing major joint replacement was associated with lower incidence of postoperative AKI, less opioid consumption, and shorter extubation time and hospital stay. However, the Dex group had higher incidence of bradycardia. We found no statistical differences in other perioperative adverse complications between the groups.

The methanol content is an important indicator in determining the quality of wine. The effects of material quality, fermentation conditions and storage containers on the formation of methanol and other substances in winemaking process were studied. The methanol content in spine grape wine was observed to increase as the fermentation temperature increased. Extending maceration time and the increasing addition of pectinase also increased the methanol content. No significant change in methanol content was observed with various yeasts added. The methanol content in wine that is stored in oak barrels was the lowest compared to that in other containers. The optimal conditions to control the methanol content in spine grape wine should be as follows: ferment wine with highquality materials under 25-26°C for 3-6 d, with a pectinase of 15-20 mg/L and yeast added; and store the wine in oak barrels.

Objective There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients’ early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models. Methods We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n=799) and test (20%;n=201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively. Results In predicting AKI, nine ML algorithms posted AUC of 0.656–1.000 in the training cohort, with the randomforest standing out and AUC of 0.674–0.821 in the test cohort, with the logistic regression model standing out. Thus, we applied the logistic regression model to establish nomogram. The nomogram was comprised of ten variables: age, body mass index, American Society of Anesthesiologists, hypoproteinemia, hypertension, diabetes, anemia, duration of low mean arterial pressure, mean arterial pressure, transfusion.The calibration curves showed good agreement between prediction and observation in both the training and test sets. Conclusion By including intraoperative and preoperative risk factors, ML algorithm can predict AKI and logistic regression model performing the best. Our prediction model and nomogram that are based on this ML algorithm can help lead decision-making for strategies to inhibit AKI over the perioperative duration.