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Liver failure often has rapid progression， multiple complications， and dangerous conditions. Acute pancreatitis is a common comorbidity during the progression of liver failure， and since acute pancreatitis has extremely similar clinical symptoms and signs to liver failure complicated by spontaneous peritonitis， it is often neglected in clinical practice. This article elaborates on the mechanisms of liver failure complicated by acute pancreatitis from the five aspects of inflammatory response， duodenal papillary dysfunction， gut microbiota dysbiosis， oxidative stress， and microcirculatory disturbance and proposes corresponding preventive measures based on these mechanisms.
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Objective:To investigate the clinical characteristics of patients with malignant tumors and immune checkpoint inhibitors (ICI) related multisystem adverse events as well as therapeutic efficacy of ICI.Methods:The general data, immune-related adverse events (irAE) type, onset time, severity and ICI efficacy of patients with malignant tumors who developed irAE after receiving ICI in China-Japan Friendship Hospital between January 2019 and November 2021 were retrospectively analyzed. All patients were divided into multisystem irAE group and single system irAE group according to whether patients with more than 1 organ or system developed irAE for once. The occurrence of irAE was summarized, and the clinical characteristics of patients were compared. Progression-free survival analysis was not performed owing to the pause of immunotherapy caused by some irAE, so the efficacy of ICI was evaluated by using ICI treatment duration (TD).Results:A total of 47 patients with malignant tumors and irAE were included in this study, with 70 times of irAE in total. The median onset time was 90 d (35 d, 196 d). Among them, 12 patients (25.53%) developed multisystem irAE (32 times of irAE in total); the other 35 patients (74.47%) developed single system irAE (38 times of irAE in total). Cutaneous toxicity for 7 times, thyroid toxicity for 7 times and pulmonary toxicity for 5 times were the most frequent among multisystem irAE group; pulmonary toxicity for 13 times, thyroid toxicity for 12 times and cutaneous toxicity for 5 times were the most frequent among single system irAE group. There were no statistically significant differences in the proportion of patients stratified by age, gender, the combination of other treatments and different body mass between the two groups (all P > 0.05). The median follow-up time was 20 months (9-40 months). The median TD of ICI was 16.00 months (95% CI 3.62-31.22 months) in multisystem irAE group and 4.60 months (95% CI 4.12-11.30 months) in single system irAE group; TD in multisystem irAE group was longer than that in single system irAE group, and the difference was statistically significant ( HR = 0.413, 95% CI 0.202-0.844, P = 0.038). Conclusions:The efficacy of ICI in patients with malignant tumors and multisystem irAE is better than that in those with single system irAE. It suggests that the better efficacy of ICI may be associated with greater risk of irAE. There is no significant difference in the clinical features between multisystem irAE and single system irAE.
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Objective:To investigate the role of three-dimensional dose distribution-based deep learning model in predicting distant metastasis of head and neck cancer.Methods:Radiotherapy and clinical follow-up data of 237 patients with head and neck cancer undergoing intensity-modulated radiotherapy (IMRT) from 4 different institutions were collected. Among them, 131 patients from HGJ and CHUS institutions were used as the training set, 65 patients from CHUM institution as the validation set, and 41 patients from HMR institution as the test set. Three-dimensional dose distribution and GTV contours of 131 patients in the training set were input into the DM-DOSE model for training and then validated with validation set data. Finally, the independent test set data were used for evaluation. The evaluation content included the area under receiver operating characteristic curve (AUC), balanced accuracy, sensitivity, specificity, concordance index and Kaplan-Meier survival curve analysis.Results:In terms of prognostic prediction of distant metastasis of head and neck cancer, the DM-DOSE model based on three-dimensional dose distribution and GTV contours achieved the optimal prognostic prediction performance, with an AUC of 0.924, and could significantly distinguish patients with high and low risk of distant metastasis (log-rank test, P<0.001). Conclusion:Three-dimensional dose distribution has good predictive value for distant metastasis in head and neck cancer patients treated with IMRT, and the constructed prediction model can effectively predict distant metastasis.
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Objective:To establish an automatic segmentation network based on different receptive fields for gross target volume (GTV) and organs at risk in patients with nasopharyngeal carcinoma.Methods:Radiotherapy data of 100 cases of nasopharyngeal carcinoma including CT images and GTV and organs at risk delineated by the physicians were collected. Ninety plans were randomly selected as the training dataset, and the other 10 plans as the validation dataset. Firstly, the images were subject to three data augmentation methods including center cropping, vertical flipping and rotation (-30°to 30°), and then input into MA_net networks proposed in this study for training. The model performance of networks was assessed by the number of network parameters (NP), floating-point number (FPN), the running memory (RM) and Dice index (DI), and eventually compared with DeeplabV3+ , PSP_net, UNet+ + and U_Net networks.Results:When the input image was in the size of 240×240, MA_net had a NP of 23.20%, 20.10%, 25.55% and 27.11% of these 4 networks, 50.02%, 19.86%, 6.37% and 13.44% for the FPN, 40.63%, 23.60%, 11.58% and 14.99% for the RM, respectively. For the DI of GTV, MA_net was 1.16%, 2.28%, 1.27% and 3.59% higher than these 4 networks. For the average DI of GTV and OAR, MA_net was 0.16%, 1.37%, 0.30% and 0.97% higher than these 4 networks.Conclusion:Compared with those four networks, the proposed MA_net network has slightly higher Dice index with fewer parameters, lower FPN and smaller RM.
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Objective:To compare the accuracy and generalized robustness of three predictive models of knowledge-based treatment strategies for radiotherapy for optimized model selection.Methods:The clinical radiotherapy plans of 45 prostate cancer (PC) cases and 25 nasopharyngeal cancer (NPC) cases were collected, and analyzed using three models (Z, L and S model), proposed by Zhu et al, Appenzoller et al and Shiraishi et al, respectively, to predict the dose-volume histogram (DVH) of bladder and rectum on PC cases and that of left and right parotid on NPC cases. The prediction error was measured by the difference of area under the predicted DVH and the clinical DVH curves (|V (pre_DVH)-V (clin_DVH)|), where a smaller prediction error implies a greater prediction accuracy. The accuracies of these three models were compared on the single organ at risk (OAR), and the generalized robustness of models was evaluated and compared by calculating the standard deviation of the prediction accuracy on different OAR. Results:For bladder and rectum, the prediction error of L model (0.114 and 0.163, respectively) was significantly higher than those values of Z and S models (≤0.071, P<0.05); for left parotid gland, the predicted error of S model (0.033) did not present significant difference from those values of Z and L models (≤0.025, P>0.05); for right parotid gland, S model (0.033) demonstrated significantly higher prediction error than those of Z and L models (≤0.028, P<0.05). Regarding different OAR, S model showed a lower standard deviation of prediction accuracy when comparing to Z and L models (0.016, 0.018 and 0.060, respectively). Conclusions:In the prediction of DVH in bladder and rectum of PC, Z and S models were more accurate than L model. In contrast, Z and L models demonstrated higher accuracy than S model in the prediction of left and right parotid glands of NPC. In respect to different OAR, the generalized robustness of S model was superior than the other two models.
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OBJECTIVE@#To investigate the allergens distribution of 576 allergic rhinitis patients in Qingyang, and to provide basic epidemiologic information for the prevention and treatment of allergic rhinitis.@*METHOD@#Skin prick test was done to all the 576 patients with allergic rhinitis with 28 kinds of allergens.@*RESULT@#Four hundred and eighty cases (83.3%) showed positive reaction to at least one allergen of 28 allergens. The most common allergens were Magwort (73.3%), Giant Ragweed (55.0%) Tree II (51.7%), Tree I (48.3%) Dermatophagoides farinae (43.3%) and Dermatophagoides pteronyssinus (36.7%). Moreover. the positivity decreased with age. There was no difference between male and female.@*CONCLUSION@#The study shows that Magwort. Giant ragweed and tree II are the most important allergens on Qingyang district.