Effect of additional equipotent fentanyl or sufentanil administration on recovery profiles during propofol-remifentanil-based anaesthesia in patients undergoing gynaecologic laparoscopic surgery: a randomized clinical trial.

BACKGROUND: In clinical practice, sufentanil has a stronger sedative effect on patients than fentanyl at equivalent doses. This study hypothesized that, at equivalent doses, patients undergoing gynaecologic laparoscopic surgery (GLS) receiving fentanyl would have an earlier emergence from anaesthesia (EA), a shorter time to extubation (TE), and a better degree of wakefulness. Therefore, this study evaluated the effects of equipotent doses of fentanyl and sufentanil on the quality of emergence in patients undergoing GLS. METHODS: One hundred seven patients scheduled for GLS under general anaesthesia were randomly divided into two groups and were induced with 0.35 µg/kg sufentanil (Group S; n = 55) or 3.5 µg/kg fentanyl (Group F; n = 52). When the GLS was almost over, the patient's abdominal cavity was flushed with warm saline, and 5 µg of sufentanil or 50 µg of fentanyl in a double-blind manner was intravenously injected into the patients. The primary outcomes of the study included EA, TE, the rate of leaving the surgical bed voluntarily and the incidence of endotracheal tube tolerance. The Ramsay Sedation Scale (RSS), and Verbal Rating Scale (VRS) scores at 15 and 30 min in the postanaesthesia care unit (PACU), as well as other adverse events, including nausea and vomiting, itching, delirium, dizziness, chills, and respiratory depression (SpO2 < 95%) in the PACU, were evaluated as secondary outcomes. RESULTS: There were no statistically significant dissimilarities between the two groups with respect to baseline characteristics. For recovery, the EA (9.0 ± 4.8 min vs. 8.9 ± 3.0 min; P = 0. 146), TE (9.5 ± 4.7 min vs. 9.0 ± 3.0 min; P = 0.135), rate of leaving the surgical bed voluntarily (31.18% vs. 38.46%; P = 0.976), and incidence of endotracheal tube tolerance (94.55% vs. 96.15%; P = 0.694) were not significantly different between the two groups. In the PACU, the 15-min RSS score (2.07 ± 0.38 vs. 2.15 ± 0.36; P = 0.125), the 30-min RSS score (2.02 ± 0.13 vs. 2.04 ± 0.19; P = 0.207), the 15-min VRS score (0.50 ± 0.57 vs. 0.67 ± 0.55; P = 0.295), and the 30-min VRS score (0.45 ± 0.50 vs. 0.75 ± 0.52; P = 0.102) were not significantly different between Groups S and F. No adverse events, such as nausea, vomiting, pruritus, delirium, and tremors, occurred in either group. The rates of respiratory depression (1.82% vs. 1.92%; P = 0.968) and dizziness (0.00% vs. 4.85%; P = 0.142) were not different between Groups S and F in the PACU. CONCLUSIONS: The majority of patients scheduled for GLS were able to rapidly and smoothly emerge from anaesthesia. After surgery, similar outcomes, including EA, TE, the incidence of endotracheal tube tolerance, the rate of leaving the surgical bed voluntarily, RSS scores, VRS scores, and adverse events in the PACU, were achieved for the patients between the two anaesthetic protocols.

An Improved Method for Broiler Weight Estimation Integrating Multi-Feature with Gradient Boosting Decision Tree.

Broiler weighing is essential in the broiler farming industry. Camera-based systems can economically weigh various broiler types without expensive platforms. However, existing computer vision methods for weight estimation are less mature, as they focus on young broilers. In effect, the estimation error increases with the age of the broiler. To tackle this, this paper presents a novel framework. First, it employs Mask R-CNN for instance segmentation of depth images captured by 3D cameras. Next, once the images of either a single broiler or multiple broilers are segmented, the extended artificial features and the learned features extracted by Customized Resnet50 (C-Resnet50) are fused by a feature fusion module. Finally, the fused features are adopted to estimate the body weight of each broiler employing gradient boosting decision tree (GBDT). By integrating diverse features with GBTD, the proposed framework can effectively obtain the broiler instance among many depth images of multiple broilers in the visual field despite the complex background. Experimental results show that this framework significantly boosts accuracy and robustness. With an MAE of 0.093 kg and an R2 of 0.707 in a test set of 240 63-day-old bantam chicken images, it outperforms other methods.