[Objective] The aim of this study was to investigate the protective role of programmed cell death ligand-1 (PD-L1) in a mouse model of spinal cord injury (SCI) by regulating T cell immunity and the PI3K/Akt/mTOR signaling pathway. [Methods] C57BL/6 mice used to establish SCI models were divided into the sham operation group (Sham), SCI group, SCI+ PD-L1 antibody group (SCI+ Ab), and SCI+ PD-L1 protein group (SCI+ PRO). c57BL/6 mice and PD-L1 knockout mice were used for SCI mapping, and they were divided into the sham operation group (Sham WT), PD-L1 knockout sham operation group (Sham PD-L1 KO), SCI model group (SCI WT), and PD-L1 knockout SCI model group (SCI PD-L1 KO). Western blotting and qRT-PCR were applied to detect the expression of PD-L1 in spinal cord tissues at different time points after SCI; mouse motor function was assessed by the Basso Mouse Motor Scale (BMS); changes in the levels of inflammatory factors and T-cell subpopulations after SCI were analyzed using qRT-PCR and flow cytometry; and Western blotting was used to detect changes in the PI3K/Akt/mTOR signaling pathway activation. [Results] PD-L1 expression was upregulated in spinal cord tissues of mice subjected to SCI palliation, peaking on day 7. Compared with the SCI PD-L1 KO group, mice in the SCI WT group had significantly higher BMS scores at 7, 14, and 28 days after SCI (P<0.05), and the levels of inflammatory factors IL-1α, IL-2, IFN-γ and TNF-α were significantly lower on day 7 after palpation (P<0.05). Compared with the SCI+ PBS group, mice in the SCI+ PRO group had significantly higher Foxp3 levels and significantly lower Th1 and Th17 levels. Foxp3 levels were significantly higher, but Th1 and Th17 cell levels were significantly lower, and Th2 and Treg cell levels were significantly higher (P<0.05). The phosphorylation of the PI3K/Akt/mTOR signaling pathway was significantly higher in the SCI WT group mice than the SCI PD-L1 KO group ones (P<0.05). In contrast, the phosphorylation of PI3K/Akt/mTOR signaling pathway was significantly lower in the SCI+ PRO group than in the SCI+ PBS group and the SCI+ Ab group (P<0.05). [Conclusion] PD-L1 plays a neuroprotective role by regulating the balance of Th1, Th17, Th2 and Treg cells and inhibiting the PI3K/Akt/mTOR signaling pathway, thereby reducing the inflammatory response after SCI. PD-L1 is expected to be a new target for the treatment of SCI.

Objective:To investigate the application value of joint friction sounds in diagnosing meniscus injury of the knee based on machine learning models.Methods:A case-control study was conducted to analyze the clinical data of 17 patients with meniscus injury of the knee (meniscus injury group) admitted to Sir Run Run Shaw Hospital Affiliated to Nanjing Medical University from August 2020 to October 2022, as well as 75 recruited healthy subjects without knee joint diseases (healthy group). The knee joint friction sounds of the subjects were collected in a relatively quiet environment (peak value below 40 dB). The sounds collected in a flexion-extension-flexion mode of exercise were split and divided randomly with a ratio of 4∶1 into the training set (125 segments from the meniscal injury group and 187 segments from the healthy group) and the test set (33 segments from the meniscal injury group and 47 segments from the healthy group). The sounds obtained in a sit-stand-sit mode of exercise were split and divided randomly with a ratio of 4∶1 into the training set (81 segments from the meniscal injury group and 164 segments from the healthy group) and the test set (20 segments from the meniscal injury group and 40 segments from the healthy group). Four machine learning models were built, including support vector machine with linear kernels, radial basis function support vector machine, random forest, and extremely randomized trees. The learning training of the model was performed on the training set, and its model performance was verified with the test set. The time required in a single collection of joint friction sound from the subjects and the interpretation of data analysis was recorded. Knee function of the subjects were scored according to the Lysholm Score before and at 1 day after the test. The accuracy rates of diagnosis of meniscus injury with friction sounds under the two modes of exercise were compared based on the test results to yield an optimal one. The effectiveness of the four models was compared to find the best machine learning model fitting the data frame of this study according to the test results such as accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC) obtained with the optimal mode of exercise. The diagnostic accuracy, misdiagnosis rate and missed diagnosis rate of joint friction sound for meniscal injury under the optimal machine learning model with the optimal mode of exercise were observed.Results:The time required in a single collection of joint friction sound ranged from 5 to 10 minutes [(7.1±1.3)minutes], when the time required for interpretation of data analysis was approximately 1 minute. The Lysholm Score before and after the test was (75.6±4.0)points and (77.7±3.7)points respectively in the meniscal injury group ( P>0.05), and (99.6±0.9)points and (99.5±1.0)points respectively in the healthy group ( P>0.05). The diagnosing accuracy rates for flexion-extension-flexion of exercise and sit-stand-sit modes of exercise were 0.775 and 0.817 under the support vector machine model with linear kernels; 0.813 and 0.900 under the radial basis function support vector machine model; 0.800 and 0.867 under the random forest model; 0.800 and 0.900 under the extremely randomized tree model. The accuracy rates for sit-stand-sit mode of exercise were all higher than those for flexion-extension-flexion mode of exercise. In the sit-stand-sit mode of exercise, the extremely randomized tree model had an accuracy rate of 0.900, sensitivity of 0.900, specificity of 0.950, F1 score of 0.900, and AUC of 0.942, which were higher than those under the remaining 3 models, showing better machine learning efficacy. Under the extremely randomized tree model in the sit-stand-sit mode of exercise, 22 (18 true positive and 4 false positive) were diagnosed as meniscal injury and 38 (36 true negative and 2 false negative) as healthy out of 60 segments in the test set (20 from the meniscal injury group and 40 from the healthy group). The diagnostic accuracy of joint friction sounds in diagnosing meniscus injury of the knee was 0.900, with the misdiagnosis rate of 0.100 and the missed diagnosis rate of 0.100. Conclusion:Diagnosis of meniscus injury of the knee with joint friction sounds can shorten time and enhance safety during the examination process. The diagnostic model using machine learning-based artificial intelligence is faster and more stable, which can be used as a diagnostic marker for such injury.