Search

Objective To evaluate the effect of intraoperative multimodal analgesia on the early postoperative quality of recovery in end-stage head and neck cancer patients undergoing open gastrostomy surgery. Methods This was a prospective, parallel, randomized controlled study. The research subjects were end-stage head and neck cancer patients who underwent elective open gastrostomy at Beijing Tongren Hospital affiliated to Capital Medical University from November 2022 to May 2023. The patients were randomly divided into local anesthesia group and multimodal analgesia group at a 1∶1 ratio. For local anesthesia group, 0.25% ropivacaine 20-30 mL was administered for local infiltration anesthesia. For multimodal analgesia group, the anesthesia method was nerve block + intravenous analgesia: ultrasound-guided left transverse abdominis plane block (0.25% ropivacaine 0.3 mL/kg)+bilateral rectus abdominis sheath block (0.25% ropivacaine 0.3 mL/kg per side)+intravenous injection of oxycodone 0.1 mg/kg, flurbiprofen 1 mg/kg, and dexamethasone 0.2 mg/kg. The primary outcome measure was the quality of requirements-15 (QoR-15) score at postoperative 24 h, while the secondary outcome measures were the QoR-15 score at postoperative 48 h, the numerical rating scale (NRS) and Bruggemann comfort scale (BCS) scores at different time points after the surgery, the first time of rescue analgesia, the first time of off-bed activity and intestinal exhaust, as well as the incidences of adverse reactions within postoperative 48 h. Results A total of 46 patients with end-stage head and neck cancer who underwent open gastrostomy and met the inclusion and exclusion criteria were ultimately enrolled, with 23 patients in multimodal analgesia group and 23 patients in local anesthesia group. There was no statistically significant difference in preoperative QoR-15 scores between the two groups (P > 0.05). Multimodal analgesia group had higher QoR-15 scores at 24 h postoperatively[(81.77±8.91) vs. (71.46±7.61), P < 0.05] and 48 h postoperatively[(86.26±7.92) vs. (80.13±6.98), P < 0.05], and the difference in QoR-15 scores at 24 h postoperatively was clinically significant. Compared with local anesthesia group, at 6 h and 24 h after surgery, the multimodal analgesia group showed a decrease in NRS scores at rest and during exercise, while the comfort BCS score increased (all P < 0.05). Multimodal analgesia group also had a delayed time for the first rescue analgesia, a reduced incidence of rescue analgesia within 48 h after surgery, an earlier time for first postoperative off-bed activity and intestinal exhaust (all P < 0.05). The incidence of adverse reactions in multimodal analgesia group and local anesthesia group was 8.70% and 13.04%, respectively, but the difference was not statistically significant (P > 0.05). Conclusion Intraoperative multimodal analgesia can effectively alleviate postoperative pain, increase the comfortable degree, shorten the first time of postoperative off-bed activity and intestinal exhaust, and accordingly improve the quality of early postoperative recovery in patients undergoing open gastrostomy.

Background Purpose Methods Results Conclusions The study focuses on thermal management in tumor‐treating fields (TTFields) therapy, crucial for patient compliance and therapeutic effectiveness. TTFields therapy, an established treatment for glioblastoma, involves applying alternating electric fields to the brain. However, managing the thermal effects generated by electrodes is a major challenge, impacting patient comfort and treatment efficiency.This research aims to explore methods for controlling temperature increases during TTFields therapy without reducing its duty cycle. The study emphasizes optimizing electrode configurations and array arrangements to mitigate temperature rise, thereby maintaining therapy effectiveness and patient compliance.Using a simplified multi‐layer tissue model and finite element analysis, various electrode configurations and array shapes were tested in COMSOL Multiphysics v6.0. Adjustments included changing the electrode gel layer radius from 8 to 12 mm, electrode spacing, and transitioning to a more uniform array arrangement, such as a square array or a circular array.The study revealed a strong correlation between high temperatures and edge current density distributions on electrodes. It was found that increasing the electrode gel layer's diameter, enlarging electrode spacing, and adopting a uniform array arrangement markedly mitigated temperature rises. By increasing the gel layer radius from the original 10 to 12 mm, a reduction in the peak temperature increases of approximately 0.3°C was observed. Changing the layout from rectangular to circular with the same area further reduced the peak temperature rise by 0.5°C. Additionally, enlarging the spacing between electrodes also contributed to temperature control. By integrating these strategies, we designed a new circular electrode array with an electrode spacing of 45 mm and a gel radius of 12 mm, successfully reducing the peak temperature from 42.1°C to 40.8°C, effectively achieving temperature control.The research demonstrates that improving electrode and array configurations can effectively manage temperature in TTFields therapy without compromising treatment duration. This strategy is crucial as TTFields therapy relies on prolonged field exposure for effectiveness. The findings offer valuable insights into thermal management in electrode array design and could lead to enhanced patient compliance and treatment efficacy in TTFields therapy. [ABSTRACT FROM AUTHOR]

Canopy volume is a crucial biological parameter for assessing tree growth, accurately estimating forest Above-Ground Biomass (AGB), and evaluating ecosystem stability. Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) are advanced precision mapping technologies that capture highly accurate point clouds for forest digitization studies. Despite advances in calculating canopy volume, challenges remain in accurately extracting the canopy and removing gaps. This study proposes a canopy volume extraction method based on an improved PointNeXt model, fusing ALS and TLS point cloud data. In this work, improved PointNeXt is first utilized to extract the canopy, enhancing extraction accuracy and mitigating under-segmentation and over-segmentation issues. To effectively calculate canopy volume, the canopy is divided into multiple levels, each projected into the xOy plane. Then, an improved Mean Shift algorithm, combined with KdTree, is employed to remove gaps and obtain parts of the real canopy. Subsequently, a convex hull algorithm is utilized to calculate the area of each part, and the sum of the areas of all parts multiplied by their heights yields the canopy volume. The proposed method's performance is tested on a dataset comprising poplar, willow, and cherry trees. As a result, the improved PointNeXt model achieves a mean intersection over union (mIoU) of 98.19% on the test set, outperforming the original PointNeXt by 1%. Regarding canopy volume, the algorithm's Root Mean Square Error (RMSE) is 0.18 m3, and a high correlation is observed between predicted canopy volumes, with an R-Square (R2) value of 0.92. Therefore, the proposed method effectively and efficiently acquires canopy volume, providing a stable and accurate technical reference for forest biomass statistics. [ABSTRACT FROM AUTHOR]