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This study examined the effects of the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count after pulmonary infection. Sprague‒Dawley rats were subjected to tracheal injection of lipopolysaccharide (LPS) to establish animal models of pulmonary infection. By inhibiting the PI3K/AKT pathway or inhibiting/inducing mitochondrial autophagy in macrophages, the severity of the pulmonary infection and the leukocyte count were altered. The PI3K/AKT inhibition group did not show a significant difference in leukocyte counts compared with the infection model group. Mitochondrial autophagy induction alleviated the pulmonary inflammatory response. The infection model group had significantly higher levels of LC3B, Beclin1, and p-mTOR than the control group. The AKT2 inhibitor group exhibited significantly increased levels of LC3B and Beclin1 compared with the control group (P < 0.05), and the Beclin1 level was significantly higher than that in the infection model group (P < 0.05). Compared with the infection model group, the mitochondrial autophagy inhibitor group exhibited significantly decreased levels of p-AKT2 and p-mTOR, whereas the levels of these proteins were significantly increased in the mitochondrial autophagy inducer group (P < 0.05). PI3K/AKT inhibition promoted mitochondrial autophagy in macrophages. Mitochondrial autophagy induction activated the downstream gene mTOR of the PI3K/AKT pathway, alleviated pulmonary inflammatory reactions, and decreased leukocyte counts.

This study investigated the molecular mechanisms underlying the involvement of the Notch signaling pathway and autophagy in the development of pulmonary fibrosis in pigeon breeder’s lung (PBL). Rats were divided into control (Ctrl), PBL model (M), M + D (Notch signaling inhibition), M + W (autophagy inhibition), and M + R (autophagy induction) groups. Lyophilized protein powder from pigeon shedding materials was used as an allergen to construct a fibrotic PBL rat model. The mechanism by which Notch signaling regulated autophagy in the pulmonary fibrosis of PBL was investigated by inhibiting the Notch pathway and interfering with autophagy. Pulmonary interstitial fibrosis was significantly greater in the M group and the M + W group than in the M + D and M + R groups. The expression of α-smooth muscle actin was significantly higher in the M, M + D, and M + W groups than in the Ctrl group (P < 0.05). The expression of the cell autophagy markers Beclin1 and LC3 was lower in the M, M + D, and M + W groups than in the Ctrl group (P < 0.05), whereas Beclin1 and LC3 expressions were higher in the M + D and M + R groups than in the M group. The levels of reactive oxygen species in serum and lung tissues were higher in the M, M + D, M + W, and M + R groups than in the Ctrl group (P < 0.05). The Notch signaling pathway is involved in the pathological process of pulmonary fibrosis in the rat model of PBL by regulating autophagy.

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

The present study aimed to investigate the association between gene methylation and leukocytopenia from the perspective of gene regulation. A total of 30 patients confirmed as having post-infection leukocytopenia at People's Hospital of Xinjiang Uygur Autonomous Region between January 2016 and June 2017 were successively recruited as the leukocytopenia group; 30 patients with post-infection leukocytosis were enrolled as the leukocytosis group. In addition, 30 healthy volunteers who received a health examination at the hospital during the same period were included as the normal control group. In each group, four individuals were randomly selected for whole genome methylation screening. After selection of key methylation sites, the remaining samples in each group were used for verification using matrix-assisted laser desorption/ionization-time of flight mass spectrometry. The levels of serum complement factors C3 and C5 in the leukocytopenia group were significantly lower than those in the other two groups (P