Zhonghua shao shang za zhi = Zhonghua shaoshang zazhi = Chinese journal of burns
-
Zhonghua Shao Shang Za Zhi · Nov 2020
[Effect and mechanism of astaxanthin on acute kidney injury in rats with full-thickness burns].
Objective: To explore the effect and mechanism of astaxanthin on acute kidney injury in rats with full-thickness burns. Methods: Forty-eight male Sprague Dawley rats of 8 to 10 weeks were divided into sham injury group, simple burn group, burn+ vehicle group, burn+ low-dose astaxanthin group, burn+ medium-dose astaxanthin group, and burn+ high-dose astaxanthin group according to the random number table, with 8 rats in each group. The back skin of rats in sham injury group were immersed in warm water of 20 ℃ for 15 s to simulate burn injury, and the back skin of rats in the other 5 groups were immersed in boiled water of 100 ℃ for 15 s to inflict full-thickness burn of 30% total body surface area. ⋯ Compared with those of simple burn group, the protein expressions of TLR4 and p-NF-кB p65 in renal tissue of rats in burn+ low-dose astaxanthin group, burn+ medium dose astaxanthin group, and burn+ high-dose astaxanthin group were significantly decreased (P<0.01). (6) The results of Western blotting combined with immunofluorescence method showed that compared with that of sham injury group, the protein expression of HO-1 in renal tissue of rats in burn+ vehicle group, burn+ low-dose astaxanthin group, burn+ medium-dose astaxanthin group, and burn+ high-dose astaxanthin group were significantly increased at PIH 48 (P<0.01), and the protein expression of HO-1 in renal tissue of rats in burn+ medium-dose astaxanthin group and burn+ high-dose astaxanthin group was significantly increased compared with that of simple burn group (P<0.01). Conclusions: Astaxanthin can attenuate the structural damage and functional decline of renal tissue and regulate the release of injury-related inflammatory factors, thus to protect the rats from acute kidney injury after burn. The HO-1/TLR4/NF-кB signaling pathway is the main regulatory mechanism of astaxanthin to achieve anti-inflammation-based renoprotection.
-
Zhonghua Shao Shang Za Zhi · Nov 2020
[Establishment and test results of an artificial intelligence burn depth recognition model based on convolutional neural network].
Objective: To establish an artificial intelligence burn depth recognition model based on convolutional neural network, and to test its effectiveness. Methods: In this evaluation study on diagnostic test, 484 wound photos of 221 burn patients in Xiangya Hospital of Central South University (hereinafter referred to as the author's unit) from January 2010 to December 2019 taken within 48 hours after injury which met the inclusion criteria were collected and numbered randomly. The target wounds were delineated by image viewing software, and the burn depth was judged by 3 attending doctors with more than 5-year professional experience in Department of Burns and Plastic Surgery of the author's unit. ⋯ Results: (1) After the testing of the test set, the precisions of the artificial intelligence burn depth recognition model for the recognition of superficial partial-thickness burn, deep partial-thickness burn, or full-thickness burn were 84% (1 095/1 301), 81% (1 215/1 499) and 82% (1 395/1 700) respectively, the recall were 73% (1 095/1 500), 81% (1 215/1 500) and 93% (1 395/1 500) respectively, and the F1_scores were 0.78, 0.81, and 0.87 respectively. (2) tSNE cloud chart showed that there was small overlapping among different burn depths in the test results for the test set of artificial intelligence burn depth recognition model, among which the overlapping between superficial partial-thickness burn and deep partial-thickness burn and that between deep partial-thickness burn and full-thickness burn were relatively more, while the overlapping between superficial partial-thickness burn and full-thickness burn was relatively less. (3) The area under the ROC curve for 3 kinds of burn depths recognized by the artificial intelligence burn depth recognition model was ≥0.94. Conclusions: The artificial intelligence burn depth recognition model established by ResNet-50 network can rather accurately identify the burn depth in the early wound photos of burn patients, especially superficial partial-thickness burn and full-thickness burn. It is expected to be used clinically to assist the diagnosis of burn depth and improve the diagnostic accuracy.