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The corrosion resistance of cement–water-glass dual-liquid slurry is poor. Improving its material properties is necessary. In this study, we examined the influence of water-based lotions on the fluidity, gelling time, and mechanical properties of a cement–water-glass dual-liquid slurry based on the mix proportion of the dual-liquid slurry commonly used in construction. The mixture ratio of a C-S (cement–water-glass slurry) dual-liquid slurry was adjusted by introducing a waterborne polyurethane lotion and a waterborne acrylic lotion to modify the traditional C-S dual-liquid slurry material. When acrylic acid is used as a modifying polymer at a dosage of 7.5%, the flowability and gelation time of the dual-liquid slurry are excellent, the compressive strength of the stone body decreases slightly, the flexural strength is improved to a certain extent, and the stone body’s crack resistance during water loss is also enhanced. Moreover, the porosity of the stone body is low.

PurposeTo investigate the effect of surgical resection on survival in gastrointestinal stromal tumors synchronous liver metastasis (GIST-SLM) and to develop clinically usable predictive models for overall survival (OS) and cancer-specific survival (CSS) in patients.MethodsWe identified patients in the SEER database diagnosed with GISTs from 2010 to 2019. We used propensity score matching (PSM) to balance the bias between the Surgery and No surgery groups. Kaplan-Meier(K-M) analysis was used to detect differences in OS and CSS between the two groups. The nomogram to predict 1, 3, and 5-year OS and CSS were developed and evaluated.ResultsAfter PSM, 228 patients were included in this study. There were significant differences in 1, 3, and 5-year OS and CSS between the two groups (OS: 93.5% vs. 84.4%, 73.2% vs. 55.3%, 60.9% vs. 36.9%, P=0.014; CSS: 3.5% vs.86.2%,75.3% vs.57.9%, 62.6% vs. 42.9%, P=0.02). We also found that patients who received surgery combined with targeted therapy had better OS and CSS at 1, 3, and 5 years than those who received surgery only (OS: 96.6% vs.90.9%, 74.9% vs. 56.8%, 61.7% vs. 35.5%, P=0.022; CSS: 96.6% vs. 92.1%, 77.4% vs.59.2%,63.8% vs. 42.0%, P=0.023). The area under the curve (AUC) was 0.774, 0.737, and 0.741 for 1, 3, and 5-year OS, respectively, with 0.782 and 0.742 for 1, 3, and 5-year CSS. In the model, C-index was 0.703 for OS and 0.705 for CSS and showed good consistency.ConclusionSurgical treatment can improve the OS and CSS of patients with GIST-SLM. In addition, the combination with chemotherapy may be more favorable for the long-term survival of patients. Meanwhile, we constructed the nomograms for predicting OS and CSS at 1, 3, and 5-year, and validated them internally. Our model can contribute to clinical management and treatment strategy optimization.

Background: The incidence of colorectal cancer is increasing worldwide, and it currently ranks third among all cancers. Moreover, pathological diagnosis is becoming increasingly arduous. Artificial intelligence has demonstrated the ability to fully excavate image features and assist doctors in making decisions. Large panoramic pathological sections contain considerable amounts of pathological information. In this study, we used large panoramic pathological sections to establish a deep learning model to assist pathologists in identifying cancerous areas on whole-slide images of rectal cancer, as well as for T staging and prognostic analysis. Methods: We collected 126 cases of primary rectal cancer from the Affiliated Hospital of Qingdao University West Coast Hospital District (internal dataset) and 42 cases from Shinan and Laoshan Hospital District (external dataset) that had tissue surgically removed from January to September 2019. After sectioning, staining, and scanning, a total of 2350 hematoxylin-eosin-stained whole-slide images were obtained. The patients in the internal dataset were randomly divided into a training cohort (n =88 ) and a test cohort (n =38 ) at a ratio of 7:3. We chose DeepLabV3+ and ResNet50 as target models for our experiment. We used the Dice similarity coefficient, accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curve, and area under the curve (AUC) to evaluate the performance of the artificial intelligence platform in the test set and validation set. Finally, we followed up patients and examined their prognosis and short-term survival to corroborate the value of T-staging investigations. Results: In the test set, the accuracy of image segmentation was 95.8%, the Dice coefficient was 0.92, the accuracy of automatic T-staging recognition was 86%, and the ROC AUC value was 0.93. In the validation set, the accuracy of image segmentation was 95.3%, the Dice coefficient was 0.90, the accuracy of automatic classification was 85%, the ROC AUC value was 0.92, and the image analysis time was 0.2 s. There was a difference in survival in patients with local recurrence or distant metastasis as the outcome at follow-up. Univariate analysis showed that T stage, N stage, preoperative carcinoembryonic antigen (CEA) level, and tumor location were risk factors for postoperative recurrence or metastasis in patients with rectal cancer. When these factors were included in a multivariate analysis, only preoperative CEA level and N stage showed significant differences. Conclusion: The deep convolutional neural networks we have establish can assist clinicians in making decisions of T-stage judgment and improve diagnostic efficiency. Using large panoramic pathological sections enables better judgment of the condition of tumors and accurate pathological diagnoses, which has certain clinical application value.

Tumor budding is considered a sign of cancer cell activity and the first step of tumor metastasis. This study aimed to establish an automatic diagnostic platform for rectal cancer budding pathology by training a Faster region-based convolutional neural network (F-R-CNN) on the pathological images of rectal cancer budding. Postoperative pathological section images of 236 patients with rectal cancer from the Affiliated Hospital of Qingdao University, China, taken from January 2015 to January 2017 were used in the analysis. The tumor site was labeled in Label image software. The images of the learning set were trained using Faster R-CNN to establish an automatic diagnostic platform for tumor budding pathology analysis. The images of the test set were used to verify the learning outcome. The diagnostic platform was evaluated through the receiver operating characteristic (ROC) curve. Through training on pathological images of tumor budding, an automatic diagnostic platform for rectal cancer budding pathology was preliminarily established. The precision–recall curves were generated for the precision and recall of the nodule category in the training set. The area under the curve = 0.7414, which indicated that the training of Faster R-CNN was effective. The validation in the validation set yielded an area under the ROC curve of 0.88, indicating that the established artificial intelligence platform performed well at the pathological diagnosis of tumor budding. The established Faster R-CNN deep neural network platform for the pathological diagnosis of rectal cancer tumor budding can help pathologists make more efficient and accurate pathological diagnoses.