Objective To evaluate the effects of different right ventricular pacing sites on left ventricular systolic synchrony using tissue Doppler imaging(TDI).Methods A tota[of sixty-nine patients with indications for permanent pacemaker implantation were enrolled sequentially by Pace-ROAD study(Pacemaker-right ventricular outflow tract and apex study,a randomized control study).They were randomized to RVOT pacing group(group A)or RVA pacing group(group B).Echocardiographic study with TDl was performed before and after 3 month follow up,and the data were analysed off-line.The peak velocity(Vs),the time to the peak of S wave(Ts)of all 12 basal and middle segments of left ventricle were measured,and then the standard deviation of Ts(Ts-SD),the average of Vs(Vs-M)were calculated.Results Thirty-six patients were randomized to group A,while the other 33 patients to group B.In each group,one patient was rejected due to non-pacing rhythm during follow-up.After 3 month pacing,the Ts-SD of group A was significantly shorter than that of group B[(23.63±2.32)ms vs(31.54±2.93)ms.P=0.0387-].In the patients with the basal Ts-SD longer than 32.6 ms(group A2 and group B2),the Ts-SD was significantly shortened than the baseline in group A2 during follow-up,while no significant difference was found in group B2.And the follow-up Ts-SD of group B2 was significantly longer than that of group A2 r(38.19±18.34)ms vs(28.55±16.93)ms,P=0.0290].Conclusions RVOT pacing is associated with favorable left ventricular systolic synchrony than RVA pacing,especially in patients with worsened baseline systolic synchrony.

Objective To evaluate the efficacy and safety of recombinant adenovirus-p53(rAdp53) injection combined with radiotherapy and hyperthermia in the treatment of unresectable advanced soft tissue sarcoma.Methods In this retrospective study, we evaluated 76 patients with unresectable advanced primary or recurrent soft tissue sarcoma treated in our hospital from November 2005 to November 2012.These patients received radiotherapy and hyperthermia with rAdp53(p53 group, n=41) or without rAdp53(control group, n=35).rAdp53((1-2)×1012viral particles each time, once a week, 8 times on average) was injected into the tumor or infused into the pelvic cavity.Radiotherapy (2 Gy each time, 5 times a week) was performed for the planning target volume at 56.3±5.3 Gy in the p53 group and 58.1±4.2 Gy in the control group, with no significant difference between the two groups (P>0.05).Superficial or deep thermotherapy was employed 8 times on average (twice a week).Clinical features, response rate, time to progression (TTP), overall survival (OS), and adverse events were compared between the two groups (P>0.05).The Kaplan-Meier method was used to calculate OS;the log-rank test was used for survival difference analysis and univariate prognostic analysis;the chi-square test was used for comparison of categorical data.Results At 2 months after treatment, the p53 group had significantly increased response rate (partial response+ complete response+ stable disease)(85% vs.54%, P=0.003) and local control rate (49% vs.23%, P=0.020) as well as prolonged TTP (12 months vs.5 months, P=0.010) and OS (48 months vs.31 months, P=0.049), as compared with the control group.No adverse events caused by radiotherapy and hyperthermia except transient fever were seen in the two groups.Conclusions Concurrent radiotherapy and hyperthermia combined with rAdp53 injection is effective and safe for patients with advanced soft tissue sarcoma.

Objective To construct a visual intelligent recognition model for Oncomelania hupensis robertsoni in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of O. hupensis robertsoni. Methods A total of 400 O. hupensis robertsoni and 400 Tricula snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 O. hupensis robertsoni and 300 Tricula snails. A total of 925 O. hupensis robertsoni images and 1 062 Tricula snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 O. hupensis robertsoni and 354 images from the remaining 100 Tricula snails served as an external test set. All acquired images were subjected to preprocessing, including cropping and resizing. Three data augmentation approaches were employed, including baseline, Mixup and Gaussian blurring, and model hyperparameters included two optimization algorithms of adaptive moment estimation (Adam) and stochastic gradient descent (SGD), two loss functions of focal loss and cross entropy loss, and two learning rate decay strategies of cosine annealing and multi-step. The intelligent recognition models of O. hupensis robertsoni and Tricula snails were constructed based on the EfficientNet-B4 model, and 7 training strategy groups were generated by combinations of different data augmentation approaches and hyperparameters. The performance of intelligent recognition models was tested with external test sets, and evaluated with accuracy, precision, recall, F1 score, loss, Youden’s index, and the area under the receiver operating characteristic curve (AUC) under different training strategies. Results The variation of loss values was comparable among intelligent recognition models with different data augmentation approaches. The Group 4 model constructed with Mixup and Gaussian blurring data augmentation approaches showed the optimal performance, with an accuracy of 90.38%, precision of 90.07%, F1 score of 89.44%, Youden’s index of 0.81 and AUC of 0.961 in the external test set. The accuracy of models using the SGD optimizer reduced by 29.16% as compared to those using the Adam optimizer (χ² = 81.325, P < 0.001), and the accuracy of models using the cross entropy loss function reduced by 0.80% as compared to the Group 4 model (χ² = 3.147, P > 0.05), while the accuracy of models using the multi-step learning rate decay strategy increased by 0.65% as compared to the Group 4 model (χ² = 0.208, P > 0.05). In addition, the model with the baseline + Mixup + Gaussianblurring data augmentation approach and hyperparameters of Adam optimizer, focal loss function and multi-step learning rate decay strategy showed the highest performance, with an accuracy of 91.03%, precision of 91.97%, recall of 88.11%, F1 score of 90.00%, Youden’s index of 0.82 and AUC values of 0.969 in external test set, respectively. Conclusions The intelligent recognition model of O. hupensis robertsoni based on EfficientNet-B4 model is accurate for identification of O. hupensis robertsoni and Tricula snails in Yunnan Province.

Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.