Objective To study the effect of inflammatory markers on the level of reactive oxygen species (ROS) and mitochondrial DNA (mtDNA) copy numbers in granulosa cells of patients without polycystic ovary syndrome (PCOS). Methods Fifty patients without PCOS treated with in vitro fertilization and embryo transfer (IVF-ET) were selected in this study. The granulosa cells were extracted and cultured in vitro. Cells were randomly divided into treatment group and control group. The 5 nmol/L interleukin (IL)-1, IL-6 and tumor necrosis factor (TNF)-αwere given to treatment group, and same amount of inflammatory diluted solution was added to control group. The levels of ROS and copy numbers of mtDNA were compared between two groups. Results The ROS levels and mtDNA copy number of granulosa cells were significantly higher in IL-1, IL-6 and TNF-αtreatment groups than those of control group (P<0.05). Conclusion Inflammatory markers of IL-1, IL-6 and TNF-αincrease the level of ROS and damage mtDNA in granulosa cells.

The research on relation between cancer and adaptive immunity is developing in depth. One of its signs is to optimize the key molecules and their pathways for regulating adaptive immunity through high-throughput molecular bioinformatics analysis. Based on the fact that cancer is an uncontrolled inflammation, adaptive immune-related cells are the main members driving the development of controllable inflammation to non-controllable inflammation, and the research on its molecular regulatory mechanism is a hot topic nowadays. Based on the in-depth sequencing database and bioinformatics analysis of the non-controllable growth (malignant transformation) of these adaptive immune-related cells, the research progress of Toll-like receptors and chemokines is summarized as follows.

To compare the performance of generalized additive model (GAM) and long short-term memory recurrent neural network (LSTM-RNN) on the prediction of daily admissions of respiratory diseases with comorbid diabetes. Daily data on air pollutants, meteorological factors and hospital admissions for respiratory diseases from Jan 1st, 2014 to Dec 31st, 2019 in Beijing were collected. LSTM-RNN was used to predict the daily admissions of respiratory diseases with comorbid diabetes, and the results were compared with those of GAM. The evaluation indexes were calculated by five-fold cross validation. Compared with the GAM, the prediction errors of LSTM-RNN were significantly lower [root mean squared error (RMSE): 21.21±3.30 vs. 46.13±7.60, <0.01; mean absolute error (MAE): 14.64±1.99 vs. 36.08±6.20, <0.01], and the value was significantly higher (0.79±0.06 vs. 0.57±0.12, <0.01). In gender stratification, RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting female admission (all <0.05), but there were no significant difference in predicting male admission between two models (all >0.05). In seasonal stratification, RMSE and MAE of LSTM-RNN were lower than those of GAM in predicting warm season admission (all <0.05), but there was no significant difference in value (>0.05). There were no significant difference in RMSE, MAE and between the two models in predicting cold season admission (all >0.05). In the stratification of functional areas, the RMSE, MAE and values of LSTM-RNN were better than those of GAM in predicting core area admission (all <0.05). has lower prediction errors and better fitting than the GAM, which can provide scientific basis for precise allocation of medical resources in polluted weather in advance.
Beijing/epidemiology* ; Diabetes Mellitus/epidemiology* ; Female ; Hospitalization ; Humans ; Male ; Memory, Short-Term ; Neural Networks, Computer