Volume 3, Issue 4

Ultrasound-guided nerve block is a safe and effective regional anesthesia technique; however, accurate identification of the brachial plexus remains challenging due to its small size and low contrast in ultrasound images. Recent advances in deep learning offer promising solutions to enhance brachial plexus segmentation and improve perioperative regional anesthesia precision and safety. This review systematically summarizes current deep learning approaches applied to ultrasound-based brachial plexus segmentation. We highlight key models, including Convolutional Neural Networks, the U-shaped Convolutional Neural Networks and their variants, Mask RegionBased Convolutional Neural Networks, and Generative Adversarial Network-based architectures, and compare their reported performances, with Dice Similarity Coefficients ranging from 0.5865 to 0.882 and Intersection over Union values up to 0.6957. Among them, U-Net remains the most frequently employed due to its balance of accuracy and computational efficiency. Moreover, novel models such as multi-objective brachial plexus segmentation network and BPMSegNet have demonstrated superior segmentation performance by incorporating attention mechanisms and spatial contrast features. Notwithstanding these advancements, challenges persist, particularly limited dataset availability and insufficient model generalization. This review provides a comprehensive overview of recent progress, evaluates comparative performance metrics, and outlines future directions to improve model robustness and clinical applicability and clinical applicability in the perioperative setting.

Objective: To compare the efficacy and adverse reactions between 0.15% ropivacaine combined with sufentanil and 0.1% ropivacaine combined with sufentanil for labor analgesia in primiparous women with severe pain. Method: 195 full-term singleton primiparous women with severe pain (visual pain assessment [VAS] ≥6) were randomly allocated to two epidural analgesia groups using different drug formulations. One group received 0.1%  ropivacaine + 0.3 μg/mL sufentanil (control group, n=98). The other group was treated with 0.15% ropivacaine and 0.3 μg/mL sufentanil (experiment group, n=97). The following parameters were recorded: analgesia onset time; maximum VAS scores before analgesia, at 20 min after epidural administration, and during labor; number of analgesic pump presses; number of rescue analgesia events; total analgesic drug consumption; modified Bromage score; maternal satisfaction; duration of labor stages; mode of delivery; neonatal Apgar scores at 1 min and 5 min; and incidence of adverse reactions during labor analgesia, such as skin itching, nausea and vomiting, urinary retention, and fever. Result: The onset time of analgesia in the experimental group was significantly shorter than that in the control group (P<0.05). While the maximum VAS scores in both groups were significantly lower at 20 minutes post-epidural administration and during labor than before delivery analgesia (P<0.05), no statistically significant inter-group differences were observed in VAS scores or in the number of pump compressions, rescue analgesia events, dosage of anesthetic drugs, modified Bromage score, or satisfaction ratings. Similarly, no significant differences were found between the two groups in the duration of labor, mode of delivery, and Apgar scores of newborns at 1 and 5 minutes, or the incidence of pruritus, nausea/vomiting, urinary retention, or intrapartum fever. Conclusion: For primiparous women with severe labor pain, initial use of 0.15% ropivacaine combined with sufentanil significantly shortens the onset time, provides more comprehensive analgesic effects, achieves higher satisfaction, and does not increase short-term adverse reactions (including motor block) compared to the conventional 0.1% concentration regimen.

Cycloastragenol, a key bioactive compound extracted from Astragalus membranaceus, has attracted increasing attention for its therapeutic potential in anti-aging, cancer treatment, and fibrosis prevention. This review summarizes the pharmacological activities of Cycloastragenol at molecular, cellular, and systemic levels, and discusses its efficacy across various disease models and potential perioperative applications. Current evidence demonstrates that Cycloastragenol exerts dose-dependent therapeutic efficacy through specific molecular targets. However, its clinical translation remains limited, particularly in surgical recovery contexts, underscoring the need for further validation through well-designed clinical trials focused on perioperative outcomes.