【摘要】 目的 研究雙側迷走神經切斷對肺缺血再灌注引起的氧化應激反應的影響。 方法 將24只健康雄性新西蘭大白兔隨機分為:假手術組（S組）、缺血再灌注組（IR組）、雙側迷走神經切斷合并缺血再灌注組（NIR組）。缺血前和再灌注末抽取動脈血進行血氣分析，觀察動脈血氧分壓PaO2及肺泡動脈氧分壓差（A-aDO2）的變化。再灌注末取肺組織檢測肺的濕干重比值（W/D）和氧化應激指標，包括丙二醛（MDA）、超氧化物歧化酶（SOD）及過氧化氫酶（CAT）。 結果 與S組比較，缺血再灌注明顯降低了PaO2，增加了A-aDO2和W/D值，增加了肺組織MDA含量并降低了SOD、CAT活性；雙側迷走神經切斷進一步降低了SOD活性。 結論 切斷實驗兔的雙側迷走神經，降低了肺組織抗氧化酶-超氧化物歧化酶的活性，提示迷走神經在降低肺缺血再灌注引起的氧化應激反應中發揮了重要的調節作用。【Abstract】 Objective To evaluate the effect of bilateral vagal nerves transection on lung ischemia-reperfusion induced oxidative stress. Methods A total of 24 New Zealand male rabbits were randomly divided into 3 groups: sham group (S group), ischemia-reperfusion group (IR group), and bilateral vagal nerves transection with ischemia-reperfusion group (NIR group). Before ischemia and at the end of reperfusion, arterial blood samples were collected for blood gas analysis. Arterial partial pressure of oxygen (PaO2) and alveolo-arterial oxygen tension difference (A-aDO2) were detected. At the end of reperfusion, lung tissues were obtained to measure wet/dry weight ratio (W/D). Evaluation of oxidative stress indicators, including content of lung malondialdehyde (MDA), superoxide dismutase enzyme (SOD) and catalase (CAT) activities was also performed. Results Compared with the S group, lung ischemia-reperfusion significantly decreased the PaO2, elevated A-aDO2 and lung W/D weight ratio. At the same time, MDA level in the lung tissue was elevated and SOD and CAT activities were decreased. After bilateral vagal nerves transection, SOD activity was further decreased. Conclusion Transection of bilateral vagal nerves reduced the activity of antioxidant enzyme, especially superoxide dismutase in lung tissue, suggesting that the integrity of the vagal nerves plays an important regulatory role in ischemia-reperfusion mediated oxidative stress in the lung.
To address the challenges in blood cell recognition caused by diverse morphology, dense distribution, and the abundance of small target information, this paper proposes a blood cell detection algorithm - the "You Only Look Once" model based on hybrid mixing attention and deep over-parameters (HADO-YOLO). First, a hybrid attention mechanism is introduced into the backbone network to enhance the model's sensitivity to detailed features. Second, the standard convolution layers with downsampling in the neck network are replaced with deep over-parameterized convolutions to expand the receptive field and improve feature representation. Finally, the detection head is decoupled to enhance the model's robustness for detecting abnormal cells. Experimental results on the Blood Cell Counting Dataset (BCCD) demonstrate that the HADO-YOLO algorithm achieves a mean average precision of 90.2% and a precision of 93.8%, outperforming the baseline YOLO model. Compared with existing blood cell detection methods, the proposed algorithm achieves state-of-the-art detection performance. In conclusion, HADO-YOLO offers a more efficient and accurate solution for identifying various types of blood cells, providing valuable technical support for future clinical diagnostic applications.