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Objective: To validate the application value of Kyoto gastritis score in diagnosing Helicobacter pylori (Hp) infection status under gastroscopy in Chinses population, and to construct a modified prediction model to improve the diagnostic value of Hp infection. Methods: A total of 749 cases who underwent gastroscopy at the Digestive Endoscopy Center of the Ninth People's Hospital (North), Shanghai Jiao Tong University School of Medicine, from January 1, 2021 to June 1, 2023 were retrospectively collected. Patients were divided into Hp-positive group and Hp-negative group based on histopathological biopsy or 13C breath test results. The endoscopic mucosal manifestations were recorded according to the Kyoto classification of gastritis, including atrophy, intestinal metaplasia, fold enlargement, diffuse redness, nodularity, RAC, red streak, and other 18 manifestations to verify the diagnostic value of Kyoto gastritis score for Hp infection. The mucosal manifestations with significant differences in univariate analysis were included in binary Logistic regression, and a modified prediction model was constructed according to the regression coefficients. ROC analysis was used to analyze the diagnostic value of Kyoto gastritis score and the modified prediction model for Hp infection. Results: The Hp infection rate was 54.47%. Intestinal metaplasia, fold enlargement, nodularity, diffuse redness, spotty redness, and sticky mucus were independent predictors of Hp infection. RAC, red streak, white and flat elevated lesion were independent predictors of Hp non-infection. The area under the curve was 0.861 (95%CI was 0.835~0.887), the accuracy was 75.2%, and the sensitivity and specificity were 88.5% and 65.1%. Mucosal manifestations such as spotty redness, sticky mucus, and RAC were incorporated into the modified prediction model. The results showed that the specificity (85.9%), positive predictive value (88.2%), negative predictive value (85.4%) and AUC [0.929 (95%CI was 0.910-0.947)] of the modified prediction model for Hp infection were higher than those of Kyoto gastritis score [65.1%, 75.2%, 82.5% and 0.861 (95%CI was 0.835-0.887), respectively]. Conclusions: Kyoto gastritis score has a certain application value for Hp in the Chinese population, and the modified prediction model has a higher diagnostic value for Hp infection.

Background: This study was conducted to investigate the clinical value of a navigation system based on deep learning and mixed reality for the treatment of kidney stones with percutaneous nephrolithotomy (PNL), and to improve its theoretical basis for the treatment of kidney stones. Methods: The data of 136 patients with kidney stones from October 2021 to December 2023 were retrospectively analyzed. All patients underwent PNL, and were categorized into a control group (Group 1) and a surgical navigation group (Group 2) according to puncture positioning method. Preoperative computed tomography (CT) was performed in both groups. In group 1, procedures were performed under standard ultrasound guidance. PNL was performed with navigation system fused with ultrasound to guide percutaneous puncture in group 2. The baseline information and procedural characteristics of both groups were compared. Results: PNL was successfully performed in both groups. No significant difference was found in the baseline date between the two groups. In group 2, real-time ultrasound images could be accurately matched with CT images with the aid of navigation system. The success rate of single puncture, puncture time, and decrease in hemoglobin were significantly improved in group 2 compared to group 1. (p < 0.05). Conclusions: The application of navigation system based on deep learning and mixed reality in PNL for kidney stones allows for real-time intraoperative navigation, with acceptable accuracy and safety. Most importantly, this technique is easily mastered, particularly by novice surgeons in the field of PNL. [ABSTRACT FROM AUTHOR]

Change detection is a critical task in remote sensing image analysis, with applications in land management and urban development. Convolutional neural networks (CNNs) have shown success in this area, but existing CNN-based methods often struggle with irrelevant changes caused by interfering factors, leading to unsatisfactory results. To address this, some studies have replaced CNN layers with transformers to enhance the receptive field and utilize global information better. However, this can make models overly reliant on global information, potentially overlooking shallow, fine-grained features, especially at the edges of changed targets. To overcome these limitations, we propose a Nested Transformer Decoder (NTD) that combines the strengths of transformers and CNNs by introducing dense skip connections. While having a large receptive field, it can also better retain shallow and fine-grained features by adding skip-connection. These innovations enable our model to more accurately identify changes and preserve the edges of the changed areas, including small changes that are difficult to detect using existing methods. Additionally, we introduce a Residual Fusion Module (RFM) to reduce false detections caused by irrelevant factors like light and shadow. The RFM employs residual information and spatiotemporal attention to suppress differences in multi-scale features, leading to better detection outcomes. The experimental results demonstrate that our proposed model outperforms the current state-of-the-art change detection methods in various metrics, and represents a novel and effective solution to the challenge of change detection in remote sensing images. Experimental results show that our model surpasses state-of-the-art change detection methods across various metrics. Tested on three public change detection datasets, our method improves the Intersection over Union (IoU) for the change class by 2.83% to 16.39% and enhances the F1-score by 1.60% to 9.63%. These results demonstrate that the proposed approach provides an effective solution to the challenges of change detection in remote sensing images. [ABSTRACT FROM AUTHOR]

The emergence of 16S rRNA and metagenomic sequencing has gradually revealed the close relationship between dysbiosis and colorectal cancer (CRC). Recent studies have confirmed that intestinal dysbiosis plays various roles in the occurrence, development, and therapeutic response of CRC. Perturbation of host immunity is one of the key mechanisms involved. The intestinal microbiota, or specific bacteria and their metabolites, can modulate the progression of CRC through pathogen recognition receptor signaling or via the recruitment, polarization, and activation of both innate and adaptive immune cells to reshape the protumor/antitumor microenvironment. Therefore, the administration of gut bacteria to enhance immune homeostasis represents a new strategy for the treatment of CRC. In this review, we cover recent studies that illuminate the role of gut bacteria in the progression and treatment of CRC through orchestrating the immune response, which potentially offers insights for subsequent transformative research. [ABSTRACT FROM AUTHOR]

In order to realize the safety, high efficiency and high recovery rate mining of 9-13 m thickness hard coal seam in Jinjitan Coal Mine, the top coal recovery rate and coal wall stability of different cutting heights are analyzed by theoretical analysis and numerical simulation. Based on the corresponding relationship between top coal recovery rate, coal wall stability and cutting height, a super large mining height and small mining and drawing ratio technology which based on mining and supplemented by releasing is proposed. The ZFY21000/35.5/70D two pillar shield hydraulic support with super large mining height is developed. Based on the optimization design of anti impact column and high-efficiency caving mechanism, the problems of easy impact of super high mining height fully mechanized top coal caving face, easy rib spalling of coal wall and easy arching of hard coal are solved. Fully mechanized top coal caving mining with super high mining height and small mining and caving ratio is an effective way to improve recovery rate and realize high yield and high efficiency of extra thick hard coal.