RESUMO
Background: Laparoscopic surgery has been in great demand over the past decades; it has also brought several obstacles, such as increasing difficulty in maintaining hemostasis, changes in surgical approach, and reduced field of vision. Locating the bleeding point can help surgeons to control bleeding quickly, however, to date, there have been no tools designed for automatic bleeding tracking in laparoscopic operations. Herein, we have proposed a spatiotemporal hybrid model based on a faster region-based convolutional neural network (RCNN) for bleeding point detection in laparoscopic surgery videos. Methods: Laparoscopic videos performed at our hospital were retrieved and images containing bleeding events were extracted. Spatiotemporal features were extracted by using red-green-blue (RGB) frames and optical flow maps and a spatiotemporal hybrid model was developed based on the faster RCNN. The proposed model contributed to (I) providing real-time bleeding point detection which directly assist surgeons, (II) showing the blood's optical flow which improved bleeding point detection, and (III) detecting both arterial and venous bleeding. Results: In this study, 12 different bleeding videos were included for deep learning model training. Compared with models containing a single RGB or a single optical flow map, our model combining RGB and optical flow achieved great detection results (precision rate of 0.8373, recall rate of 0.8034, and average precision of 0.6818). Conclusions: Our approach performs well in bleeding point location and recognition, indicating its potential value in helping to maintain and re-establish hemostasis during operations.
RESUMO
Method: 108 IS samples and 47 matched controls were obtained from the GEO database. Immune-related genes (IRGs) and their associated drugs were collected from the ImmPort and PharmGBK databases, respectively. Random forest (RF) regression and least absolute shrinkage and selection operator (LASSO) logistic regression were applied to identify immune-related genetic biomarkers (IRGBs) of IS, and accuracy was verified using neural network models. Finally, proportion changes of various immune cells in peripheral blood of IS patients were evaluated using CIBERSORT and xCell and correlation analyses were performed between IRGBs and differentially distributed immune cells. Results: A total of 537 genes were differentially expressed between IS and control samples. Four immune-related differential expressed genes identified by regression analysis presented strong predictive power (AUC = 0.909) which we suggeseted them as immune-related genetic biomarkers (IRGBs). We also demonstrated six immune-related genes targeted by known drugs. In addition, post-IS immune system presented an increase in the proportion of innate immune cells and a decrease in adaptive immune cells in the peripheral circulation, and IRGBs showing significance were associated with this process. Conclusion: The study identified CARD11, ICAM2, VIM, and CD19 as immune-related genetic biomarkers of IS. Six immune-related DEGs targeted by known drugs were found and provide new candidate drug targets for modulating the post-IS immune system. The innate immune cells and adaptive immune cells are diversified in the post-IS immune system, and IRGBs might play important role during this process.