Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis.

Ulcerative colitis (UC) is a chronic and recurrent inflammatory disease that affects the colon and rectum. The response to treatment varies among individuals with UC. Therefore, the aim of this study was to identify and explore potential biomarkers for different subtypes of UC and examine their association with immune cell infiltration. We obtained UC RNA sequencing data from the GEO database, which included the training set GSE92415 and the validation set GSE87473 and GSE72514. UC patients were classified based on GLS and its associated genes using consensus clustering analysis. We identified differentially expressed genes (DEGs) in different UC subtypes through a differential expression analysis of the training cohort. Machine learning algorithms, including Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were utilized to identify marker genes for UC. The CIBERSORT algorithm was used to determine the abundance of various immune cells in UC and their correlation with UC signature genes. Finally, we validated the expression of GLS through in vivo and ex vivo experiments. The expression of GLS was found to be elevated in patients with UC compared to normal patients. GLS and its related genes were able to classify UC patients into two subtypes, C1 and C2. The C1 subtype, as compared to the C2 subtype, showed a higher Mayo score and poorer treatment response. A total of 18 DEGs were identified in both subtypes, including 7 up-regulated and 11 down-regulated genes. Four UC signature genes (CWH43, HEPACAM2, IL24, and PCK1) were identified and their diagnostic value was validated in a separate cohort (AUC > 0.85). Furthermore, we found that UC signature biomarkers were linked to the immune cell infiltration. CWH43, HEPACAM2, IL24, and PCK1 may serve as potential biomarkers for diagnosing different subtypes of UC, which could contribute to the development of targeted molecular therapy and immunotherapy for UC.

Identification of glutamine metabolism-related gene signature to predict colorectal cancer prognosis.

Backgrounds: Colorectal cancer (CRC) is a highly malignant gastrointestinal malignancy with a poor prognosis, which imposes a significant burden on patients and healthcare providers globally. Previous studies have established that genes related to glutamine metabolism play a crucial role in the development of CRC. However, no studies have yet explored the prognostic significance of these genes in CRC. Methods: CRC patient data were downloaded from The Cancer Genome Atlas (TCGA), while glutamine metabolism-related genes were obtained from the Molecular Signatures Database (MSigDB) database. Univariate COX regression analysis and LASSO Cox regression were utilized to identify 15 glutamine metabolism-related genes associated with CRC prognosis. The risk scores were calculated and stratified into high-risk and low-risk groups based on the median risk score. The model's efficacy was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis. Cox regression analysis was employed to determine the risk score as an independent prognostic factor for CRC. Differential immune cell infiltration between the high-risk and low-risk groups was assessed using the ssGSEA method. The clinical applicability of the model was validated by constructing nomograms based on age, gender, clinical staging, and risk scores. Immunohistochemistry (IHC) was used to detect the expression levels of core genes. Results: We identified 15 genes related to glutamine metabolism in CRC: NLGN1, RIMKLB, UCN, CALB1, SYT4, WNT3A, NRCAM, LRFN4, PHGDH, GRM1, CBLN1, NRG1, GLYATL1, CBLN2, and VWC2. Compared to the high-risk group, the low-risk group demonstrated longer overall survival (OS) for CRC. Clinical correlation analysis revealed a positive correlation between the risk score and the clinical stage and TNM stage of CRC. Immune correlation analysis indicated a predominance of Th2 cells in the low-risk group. The nomogram exhibited excellent discriminatory ability for OS in CRC. Immunohistochemistry revealed that the core gene CBLN1 was expressed at a lower level in CRC, while GLYATL1 was expressed at a higher level. Conclusions: In summary, we have successfully identified and comprehensively analyzed a gene signature associated with glutamine metabolism in CRC for the first time. This gene signature consistently and reliably predicts the prognosis of CRC patients, indicating its potential as a metabolic target for individuals with CRC.

Psychological factors enhanced heterogeneous learning interactive graph knowledge tracing for understanding the learning process.

Introduction: With the rapid expansion of online education, there is a burgeoning interest within the EdTech space to offer tailored learning experiences that cater to individual student's abilities and needs. Within this framework, knowledge tracing tasks have garnered considerable attention. The primary objective of knowledge tracing is to develop a model that assesses a student's proficiency in a particular skill based on their historical performance in exercises, enabling predictions regarding the likelihood of correct responses in future exercises. While existing knowledge tracing models often incorporate information such as students' exercise answering history and skill mastery level, they frequently overlook the students' mental states during the learning process. Methods: This paper addresses this gap by introducing a novel psychological factors-enhanced heterogeneous learning interactive graph knowledge tracing model (Psy-KT). This model delineates the interactions among students, exercises, and skills through a heterogeneous graph, supplementing it with four psychological factors that capture students' mental states during the learning process: frustration level, confusion level, concentration level, and boredom level. In the modeling of students' learning processes, we incorporate the forgetting curve and construct relevant cognitive parameters from the features. Additionally, we employ the Item Response Theory (IRT) model to predict students' performance in answering exercises at the subsequent time step. This model not only delves into the psychological aspects of students during the learning process but also integrates the simulation of forgetting, a natural phenomenon in the learning journey. The inclusion of cognitive parameters enhances the description of changes in students' abilities throughout the learning process. This dual focus allows for a more comprehensive understanding of students' learning behaviors while providing a high level of interpretability for the model. Results and discussion: Empirical validation of the Psy-KT model is conducted using four publicly available datasets, demonstrating its superior performance in predicting students' future performance. Through rigorous experimentation, the integration of psychological and forgetting factors in the Psy-KT model not only improves predictive accuracy but also enables educators to offer more targeted tutoring and advice, enhancing the overall efficacy of the learning experience.

Comparable long­term survival outcomes of endoscopic treatment versus surgical treatment for gastrointestinal stromal tumors with a diameter of 5-10 cm.

Currently, endoscopic treatment for small gastrointestinal stromal tumors (GIST) has been widely accepted. However, for tumors larger than 5 cm, endoscopic treatment has not been recognized by national guidelines as the standard therapy due to concerns about safety and adverse tumor outcomes. Therefore, this study compares the long-term survival outcomes of endoscopic treatment and surgical treatment for GIST in the range of 5-10 cm. We selected patients with GIST from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. Kaplan-Meier analysis and the log-rank test were employed to compare the long-term survival outcomes between endoscopic treatment and surgical treatment. A multivariate Cox proportional hazards model was used for analysis to identify risk factors influencing patient prognosis. To balance baseline data, we performed 1:1 propensity score matching (PSM). A total of 1223 GIST patients were included, with 144 patients (11.8%) received endoscopic treatment and 1079 patients (88.2%) received surgical treatment. Before PSM, there was no significant difference in the long-term survival rates between the two groups [5-year OS (86.5% vs. 83.5%, P = 0.42), 10-year OS (70.4% vs. 66.7%, P = 0.42)]. After adjusting for covariates, we found that the overall survival (HR = 1.26, 95% CI 0.89-1.77, P = 0.19) and cancer-specific survival (HR = 1.69, 95% CI 0.99-2.89, P = 0.053) risks were comparable between the endoscopic treatment group and the surgical treatment group. In the analysis after PSM, there was no significant difference between the endoscopic treatment group and the surgical treatment group. Our study found that for GIST patients with tumor sizes between 5 and 10 cm, the long-term OS and CSS outcomes were similar between the endoscopic treatment group and the surgical treatment group.

Biological functions of circRNA in regulating the hallmarks of gastrointestinal cancer (Review).

Circular RNA (circRNA) was first observed in the cytoplasm of eukaryotic cells in 1979, but it was not characterized in detail until 2012, when high­throughput sequencing technology was more advanced and available. Consequently, the mechanism of circRNA formation and its biological function have been progressively elucidated by researchers. circRNA is abundant in eukaryotic cells and exhibits a certain degree of organization, timing and disease­specificity. Additionally, it is poorly degradable, meeting the characteristics of an ideal clinical biomarker. In the present review, the recent research progress of circRNAs in digestive tract malignant tumors was primarily discussed. This included the roles, biological functions and clinical significance of circRNA, providing references for its research value and clinical potential in gastrointestinal cancer.

SERPINB5 promotes colorectal cancer invasion and migration by promoting EMT and angiogenesis via the TNF-α/NF-κB pathway.

This study aimed to investigate the role of SERPINB5 in colorectal cancer (CRC). We established knockdown and overexpression models of SERPINB5 in CRC cells and conducted bioinformatics analysis to assess the clinicopathological significance of SERPINB5 expression in CRC patients. Human CRC cells were transfected with LV-SERPINB5 and sh-SERPINB5 lentivirus for subsequent functional and mechanistic studies. Results showed that high SERPINB5 expression correlated positively with CEA levels, N stage and lymphatic infiltration, while displaying a negative correlation with progression-free survival. Overexpression of SERPINB5 in CRC cells upregulated the expression of TNF-α, p-NF-κB/p65, N-cadherin, MMP2 and MMP9, accompanied by decreased E-cadherin expression. In addition, SERPINB5 overexpression enhanced the migration, invasion, and proliferation of CRC cells. Furthermore, overexpression of SERPINB5 in CRC cells increased VEGFA expression, and the conditioned medium from SERPINB5-overexpressing CRC cells promoted tube formation of HUVECs. Conversely, overexpression of SERPINB5 in HUVECs decreased VEGFA expression and inhibited tube formation. Notably, these changes in CRC cells were reversed by QNZ, a specific inhibitor of the TNF-α/NF-κB pathway. In summary, our findings revealed that high SERPINB5 expression correlated with poor progression-free survival in CRC patients. Moreover, SERPINB5 could induce EMT and angiogenesis by activating the TNF-α/NF-κB pathway, thereby promoting the invasion and migration of CRC cells.

Development and validation of a preoperative risk nomogram prediction model for gastric gastrointestinal stromal tumors.

BACKGROUND AND STUDY AIMS: Gastrointestinal stromal tumors (GIST) carry a potential risk of malignancy, and the treatment of GIST varies for different risk levels. However, there is no systematic preoperative assessment protocol to predict the malignant potential of GIST. The aim of this study was to develop a reliable and clinically applicable preoperative nomogram prediction model to predict the malignant potential of gastric GIST. PATIENTS AND METHODS: Patients with a pathological diagnosis of gastric GIST from January 2015 to December 2021 were screened retrospectively. Univariate and multivariate logistic analyses were used to identify independent risk factors for gastric GIST with high malignancy potential. Based on these independent risk factors, a nomogram model predicting the malignant potential of gastric GIST was developed and the model was validated in the validation group. RESULTS: A total of 494 gastric GIST patients were included in this study and allocated to a development group (n = 345) and a validation group (n = 149). In the development group, multivariate logistic regression analysis revealed that tumor size, tumor ulceration, CT growth pattern and monocyte-to- lymphocyte ratio (MLR) were independent risk factors for gastric GIST with high malignancy potential. The AUC of the model were 0.932 (95% CI 0.890-0.974) and 0.922 (95% CI 0.868-0.977) in the development and validation groups, respectively. The best cutoff value for the development group was 0.184, and the sensitivity and specificity at this value were 0.895 and 0.875, respectively. The calibration curves indicated good agreement between predicted and actual observed outcomes, while the DCA indicated that the nomogram model had clinical application. CONCLUSIONS: Tumor size, tumor ulceration, CT growth pattern and MLR are independent risk factors for high malignancy potential gastric GIST, and a nomogram model developed based on these factors has a high ability to predict the malignant potential of gastric GIST.

Are Radiomic Spleen Features Useful for Assessing the Response to Infliximab in Patients With Crohn's Disease? A Multicenter Study.

INTRODUCTION: To develop and validate a radiomics nomogram for assessing the response of patients with Crohn's disease (CD) to infliximab. METHODS: Radiomics features of the spleen were extracted from computed tomography enterography images of each patient's arterial phase. The feature selection process was performed using the least absolute shrinkage and selection operator algorithm, and a radiomics score was calculated based on the radiomics signature formula. Subsequently, the radiomic model and the clinical risk factor model were separately established based on the radiomics score and clinically significant features, respectively. The performance of both models was evaluated using receiver operating characteristic curves, decision curve analysis curves, and clinical impact curves. RESULTS: Among the 175 patients with CD, 105 exhibited a clinical response, and 60 exhibited clinical remission after receiving infliximab treatment. Our radiomic model, comprising 20 relevant features, demonstrated excellent predictive performance. The radiomic nomogram for predicting clinical response showed good calibration and discrimination in the training cohort (area under the curve [AUC] 0.909, 95% confidence interval [CI] 0.840-0.978), the validation cohort (AUC 0.954, 95% CI 0.889-1), and the external cohort (AUC = 0.902, 95% CI 0.83-0.974). Accordingly, the nomogram was also suitable for predicting clinical remission. Decision curve analysis and clinical impact curves highlighted the clinical utility of our nomogram. DISCUSSION: Our radiomics nomogram is a noninvasive predictive tool constructed from radiomic features of the spleen. It also demonstrated good predictive accuracy in evaluating CD patients' response to infliximab treatment. Multicenter validation provided high-level evidence for its clinical application.

A novel atrial fibrillation automatic detection algorithm based on ensemble learning and multi-feature discrimination.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia disorder that necessitates long-time electrocardiogram (ECG) data for clinical diagnosis, leading to low detection efficiency. Automatic detection of AF signals within short-time ECG recordings is challenging. To address these issues, this paper proposes a novel algorithm called Ensemble Learning and Multi-Feature Discrimination (ELMD) for the identification and detection of AF signals. Firstly, a robust classifier, BSK-Model, is constructed using ensemble learning. Subsequently, the ECG R-waves are detected, and the ECG signals are segmented into consecutive RR intervals. Time domain, frequency domain, and nonlinear features are extracted from these intervals. Finally, these features are fed into the BSK-Model to discriminate AF. The proposed methodology is evaluated using the MIT-BIH AF database. The results demonstrate that when RR intervals are employed as classification units, the specificity and accuracy of AF detection in long-time ECG data exceed 99%, showcasing a significant improvement over traditional single-model classification. Additionally, the sensitivity and accuracy achieved by testing cardiac segments are both above 96%. With a minimum requirement of only four cardiac segments, AF events can be accurately identified, thereby enabling rapid discrimination of short-time single-lead ECG AF events. Consequently, this approach is suitable for real-time and accurate AF detection using low-computational-power ECG diagnostic analysis devices, such as wearable devices.

Exploring the Molecular Targets and Therapeutic Potential of Coptisine in Colon Cancer: A Network Pharmacology Approach.

INTRODUCTION: Colon cancer is a frequent malignancy, and surgery is still the primary therapy for people with colon cancer. Other treatments, including radiation, chemotherapy, and biologic therapy, may be utilized as a supplement. Chemotherapy, a prominent treatment for colon cancer, has failed to provide positive outcomes. This necessitates the development of more effective and less harmful treatment drugs. Coptisine was discovered to inhibit the development of colon cancer cell line HCT-116 in vivo, decrease the growth of HCT-116 cells, and cause apoptosis in vitro in colon cancer. Coptisine (COP) has shown antitumor activity in colon cancer, but its molecular mechanism and its molecular targets have not been fully understood. METHODS: In this study, the biological behavior was verified in vitro. The targets of Huanglian alkaloids on colon cancer were predicted, and the protein-protein interaction (PPI) network was constructed. The core targets of safranine for colon cancer were extracted and analyzed by GO and KEGG enrichment to identify the possible molecular mechanisms of safranine treatment. Western blot was used to detect the changes of related pathway proteins in colon cancer cells. The differential expression of hub genes in colon cancer was analyzed using the GEPIA2 website. The binding ability of safranine to the target was verified by molecular docking. Finally, the targets were preliminarily verified by q-PCR analysis. RESULTS: Coptisine can inhibit the survival, migration, and proliferation of colon cancer cells DLD1 and HCT-116. Based on network pharmacology, ninety-one targets for colon cancer were screened. ESR1, ALB, AR, CDK2, PARP1, HSP90AB1, IGF1R, CCNE1, and CDC42 were found in the top 10. Enrichment analysis showed that these targets were mainly related to pathways in cancer, FC γ R-mediated phagocytosis, prostate cancer, progesterone-mediated oocyte maturation, the oestrogen signal pathway, proteoglycan in cancer and the PI3K-Akt signal pathway. WB results showed that after the treatment of colon cancer DLD1 cells with coptisine, the expression of P-AKT and AKT decreased, that of its downstream protein Bcl-2 decreased, and that of BAX increased. Differential expression analysis of hub genes showed that CCNE1, CDK2, HSP90AB1, and CHEK2 were upregulated in colon cancer samples, and molecular docking showed that these targets had a good ability to bind to coptisine. After the treatment of colon cancer DLD1 cells with coptisine, q-PCR results showed that CCNE1 and HSP90AB1 were significantly downregulated, while CDK2 and CHEK2 had no significant changes. CONCLUSION: Coptisine may be a candidate drug for the treatment of colon cancer, and its therapeutic effect may be related to the cancer pathway and PI3K-Akt signalling pathway. CCNE1 and HSP90AB1 may be potential targets of coptisine in the treatment of colon cancer.

Intrinsic Polarized Electric Field Induces a Storing Mechanism to Achieve Energy Storing Catalysis in V2 C MXene.

Efficient storage and separation of holes and electrons pose significant challenges for catalytic reactions, particularly in the context of single-phase catalysis. Herein, V2 C MXene, with its intrinsic polarized electric field, successfully overcomes this obstacle. To enhance hole storage, a multistep etching process is employed under reducing conditions to control the content of surface termination groups, thus exposing more defective active sites. The intrinsically polarized electric field confines holes to the surface of the layer and free electrons within the layer, leading to a lag in e- release compared to h+ . The quantities of stored holes and electrons are measured to be 18.13 µmol g-1 and 106.37 µmol g-1 , respectively. Under dark, V2 C demonstrates excellent and stable dark-catalytic performance, degrading 57.91% of tetracycline (TC 40 mg L-1 ) and removing 23% of total organic carbon (TOC) after 140 min. In simulated sunlight and near-infrared light, the corresponding degradation rates reach 72.24% and 79.54%, with corresponding TOC removal rates of 49% and 48%, respectively. The hole and electron induced localized surface plasmon resonance (LSPR) effects contribute to a long-lasting and enhanced broad-spectrum mineralization of V2 C MXene. This study provides valuable insights into the research and application of all-weather MXene energy storage catalytic materials.

Multi-Channel Representation Learning Enhanced Unfolding Multi-Scale Compressed Sensing Network for High Quality Image Reconstruction.

Deep Unfolding Networks (DUNs) serve as a predominant approach for Compressed Sensing (CS) reconstruction algorithms by harnessing optimization. However, a notable constraint within the DUN framework is the restriction to single-channel inputs and outputs at each stage during gradient descent computations. This constraint compels the feature maps of the proximal mapping module to undergo multi-channel to single-channel dimensionality reduction, resulting in limited feature characterization capabilities. Furthermore, most prevalent reconstruction networks rely on single-scale structures, neglecting the extraction of features from different scales, thereby impeding the overall reconstruction network's performance. To address these limitations, this paper introduces a novel CS reconstruction network termed the Multi-channel and Multi-scale Unfolding Network (MMU-Net). MMU-Net embraces a multi-channel approach, featuring the incorporation of Adap-SKConv with an attention mechanism to facilitate the exchange of information between gradient terms and enhance the feature map's characterization capacity. Moreover, a Multi-scale Block is introduced to extract multi-scale features, bolstering the network's ability to characterize and reconstruct the images. Our study extensively evaluates MMU-Net's performance across multiple benchmark datasets, including Urban100, Set11, BSD68, and the UC Merced Land Use Dataset, encompassing both natural and remote sensing images. The results of our study underscore the superior performance of MMU-Net in comparison to existing state-of-the-art CS methods.

Exploring the Targets and Molecular Mechanisms of Thalidomide in the Treatment of Ulcerative Colitis: Network Pharmacology and Experimental Validation.

BACKGROUND: Ulcerative colitis (UC) is a chronic, nonspecific, inflammatory disease of the intestine with an unknown cause. Thalidomide (THA) has been shown to be an effective drug for the treatment of UC. However, the molecular targets and mechanism of action of THA for the treatment of UC are not yet clear. OBJECTIVES: Combining network pharmacology with in vitro experiments, this study aimed to investigate the potential targets and molecular mechanisms of THA for the treatment of UC. METHODS: Firstly, relevant targets of THA against UC were obtained from public databases. Then, the top 10 hub targets and key molecular mechanisms of THA for UC were screened based on the network pharmacology approach and bioinformatics method. Finally, an in vitro cellular inflammation model was constructed using lipopolysaccharide (LPS) induced intestinal epithelial cells (NCM460) to validate the top 10 hub targets and key signaling pathways. RESULTS: A total of 121 relevant targets of THA against UC were obtained, of which the top 10 hub targets were SRC, LCK, MAPK1, HSP90AA1, EGFR, HRAS, JAK2, RAC1, STAT1, and MAP2K1. The PI3K-Akt pathway was significantly associated with THA treatment of UC. In vitro experiments revealed that THA treatment reversed the expression of HSP90AA1, EGFR, STAT1, and JAK2 differential genes. THA was able to up- regulate the mRNA expression of pro-inflammatory factor IL-10 and decrease the mRNA levels of anti-inflammatory factors IL-6, IL-1ß, and TNF-α. Furthermore, THA also exerted anti-inflammatory effects by inhibiting the activation of the PI3K/Akt pathway. CONCLUSION: THA may play a therapeutic role in UC by inhibiting the PI3K-Akt pathway. HSP90AA1, EGFR, STAT1, and JAK2 may be the most relevant potential therapeutic targets for THA in the treatment of UC.

The E3 ubiquitin ligase RNF31 mediates the development of ulcerative colitis by regulating NLRP3 inflammasome activation.

Ulcerative colitis (UC) is characterized by dysregulated inflammation and disruption of the intestinal barrier. The NLRP3 inflammasome, which is composed of NLRP3, ASC, and caspase-1, plays a crucial role in UC pathogenesis by triggering the production of proinflammatory cytokines. In this study, we investigated the regulatory role of RNF31 in NLRP3 inflammasome activation during UC development. Through comprehensive analysis of ulcerative colitis tissues using the GEO database and immunohistochemistry, we found that RNF31 expression was elevated in UC tissues, which prompted further investigation into its function. We constructed an RNF31 knockdown cell model and observed a significant reduction in NLRP3 inflammasome activation, indicating the involvement of RNF31 in regulating NLRP3. Mechanistically, RNF31 could interact with NLRP3 through the RBR structural domain, leading to increased K63-linked ubiquitination of NLRP3 and consequent stabilization. Coimmunoprecipitation experiments revealed a mutual interaction between RNF31 and NLRP3, substantiating their functional association. Finally, an in vivo mouse model with RNF31 knockdown showed a notable reduction in NLRP3 expression, which was accompanied by a decrease in the proinflammatory cytokines IL-18 and IL-1ß. The successful attenuation of DSS-induced tissue inflammation by this treatment confirmed the physiological relevance of RNF31-mediated regulation of NLRP3. This study unveils a novel regulatory pathway by which RNF31 affects NLRP3 inflammasome activation, providing new insights into UC pathogenesis and potential therapeutic targets for UC treatment.

NLRP3: A Promising Therapeutic Target for Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD), which includes Crohn's disease and ulcerative colitis, is an intestinal disease with complicated pathological mechanisms. The incidence of IBD has been increasing in recent years, which has a significant negative impact on the lives of patients. Therefore, it is particularly important to find new therapeutic targets and innovative drugs for the development of IBD. Recent studies have revealed that NLRP3 inflammatory vesicles can play an important role in maintaining intestinal homeostasis and sustaining the intestinal immune response in IBD. On the one hand, aberrant activation of NLRP3 inflammatory vesicles may cause excessive immune response by converting caspase-1, proIL-18, and proIL-1ß to their active forms and releasing pro-inflammatory cytokines to stimulate the development and progression of IBD, and we can improve IBD by targeting blockade of NLRP3 activation. On the other hand, NLRP3 may also play an enter protective role by maintaining the homeostasis of the intestinal immune system. In this paper, we reviewed the activation mechanism of NLRP3 inflammasome, and the effects of NLRP3 inflammasome activation on IBD are discussed from two different perspectives: pathology and protection. At the same time, we listed the effects of direct inhibitors, indirect inhibitors, and natural inhibitors of NLRP3 inflammasome on IBD in combination with cutting-edge advances and clinical practice results, providing new targets and new ideas for the clinical treatment of IBD.

Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms.

A robust and scientifically grounded teaching evaluation system holds significant importance in modern education, serving as a crucial metric that reflects the quality of classroom instruction. However, current methodologies within smart classroom environments have distinct limitations. These include accommodating a substantial student population, grappling with object detection challenges due to obstructions, and encountering accuracy issues in recognition stemming from varying observation angles. To address these limitations, this paper proposes an innovative data augmentation approach designed to detect distinct student behaviors by leveraging focused behavioral attributes. The primary objective is to alleviate the pedagogical workload. The process begins with assembling a concise dataset tailored for discerning student learning behaviors, followed by the application of data augmentation techniques to significantly expand its size. Additionally, the architectural prowess of the Extended-efficient Layer Aggregation Networks (E-ELAN) is harnessed to effectively extract a diverse array of learning behavior features. Of particular note is the integration of the Channel-wise Attention Module (CBAM) focal mechanism into the feature detection network. This integration plays a pivotal role, enhancing the network's ability to detect key cues relevant to student learning behaviors and thereby heightening feature identification precision. The culmination of this methodological journey involves the classification of the extracted features through a dual-pronged conduit: the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN). Empirical evidence vividly demonstrates the potency of the proposed methodology, yielding a mean average precision (mAP) of 96.7%. This achievement surpasses comparable methodologies by a substantial margin of at least 11.9%, conclusively highlighting the method's superior recognition capabilities. This research has an important impact on the field of teaching evaluation system, which helps to reduce the burden of educators on the one hand, and makes teaching evaluation more objective and accurate on the other hand.

Identification of a Diagnosis and Therapeutic Inflammatory Response-Related Gene Signature Associated with Esophageal Adenocarcinoma.

The purpose of this study is to identify the key regulatory genes related to the inflammatory response of esophageal adenocarcinoma (EAC) and to find new diagnosis and therapeutic options. We downloaded the dataset GSE72874 from the Gene Expression Omnibus database for this study. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to find common inflammatory response-related genes (IRRGs) in EAC. The relationship between normal and tumor immune infiltration was analyzed using an online database of CIBERSORTx. Finally, 920 DEGs were identified, of which 5 genes were key IRRGs associated with EAC, including three down-regulated genes GNA15, MXD1, and NOD2, and two down-regulated genes PLAUR and TIMP1. Further research found that GNA15, MXD1, and NOD2 were down-regulated, PLAUR and TIMP1 were up-regulated in Barrett's esophagus (BE). In addition, we found that the expression of GNA15 and MXD1 in normal esophageal squamous epithelial cells decreased after ethanol treatment, while the expression of PLAUR and TIMP1 increased after ethanol treatment. Compared with normal esophageal tissue, immune cells infiltrated such as plasma cells, macrophages M0, macrophages M1, macrophages M2, dendritic cells activated, and mast cells activated were significantly increased in EAC, while immune cells infiltrated such as T cells CD4 memory resting, T cells follicular helper, NK cells resting, and dendritic cells resting were significantly reduced. The receiver operating characteristic curve indicated that GNA15, MXD1, NOD2, PLAUR and TIMP1 expression had a performed well in diagnosing EAC from healthy control. GNA15, MXD1, NOD2, PLAUR and TIMP1 were identified and validated as novel potential biomarkers for early diagnosis and may be new molecular targets for treatment of EAC.

Prediction model for lymph node metastasis in superficial colorectal cancer: a better choice than computed tomography.

BACKGROUND: Risk evaluation of lymph node metastasis (LNM) in superficial colorectal cancer resected by endoscopic surgery is critical for determining subsequent therapeutic strategies, but the role of existing clinical methods, including computed tomography, remains limited. METHODS: Features of the nomogram were determined by logistic regression analysis, and the performance was validated by calibration plots, ROC curves and DCA curves in both the training set and the validation set. RESULTS: A total of 608 consecutive superficial CRC cases were randomly divided into 426 training and 182 validation cases. Univariate and multivariate logistic regression analyses revealed that age < 50, tumour budding, lymphatic invasion and lower HDL levels were risk factors for LNM. Stepwise regression and the Hosmer‒Lemeshow goodness of fit test showed that the nomogram had good performance and discrimination, which was validated by ROC curves and calibration plots. Internal and external validation demonstrated that the nomogram had a higher C-index (training group, 0.749, validation group, 0.693). DCA and clinical impact curves graphically show that the use of the nomogram to predict LNM had remarkable predictive power. Finally, in comparison with CT diagnosis, the nomogram also visually showed higher superiority, as demonstrated by ROC, DCA and clinical impact curves. CONCLUSION: Using common clinicopathologic factors, a noninvasive nomogram for individualized prediction of LNM after endoscopic surgery was conveniently established. Nomograms have great superiority in the risk stratification of LNM compared with traditional CT imaging.

Development and Characterization of a Novel Wheat-Tetraploid Thinopyrum elongatum 6E (6D) Disomic Substitution Line with Stripe Rust Resistance at the Adult Stage.

Stripe rust, which is caused by Puccinia striiformis f. sp. tritici, is one of the most devastating foliar diseases of common wheat worldwide. Breeding new wheat varieties with durable resistance is the most effective way of controlling the disease. Tetraploid Thinopyrum elongatum (2n = 4x = 28, EEEE) carries a variety of genes conferring resistance to multiple diseases, including stripe rust, Fusarium head blight, and powdery mildew, which makes it a valuable tertiary genetic resource for enhancing wheat cultivar improvement. Here, a novel wheat-tetraploid Th. elongatum 6E (6D) disomic substitution line (K17-1065-4) was characterized using genomic in situ hybridization and fluorescence in situ hybridization chromosome painting analyses. The evaluation of disease responses revealed that K17-1065-4 is highly resistant to stripe rust at the adult stage. By analyzing the whole-genome sequence of diploid Th. elongatum, we detected 3382 specific SSR sequences on chromosome 6E. Sixty SSR markers were developed, and thirty-three of them can accurately trace chromosome 6E of tetraploid Th. elongatum, which were linked to the disease resistance gene(s) in the wheat genetic background. The molecular marker analysis indicated that 10 markers may be used to distinguish Th. elongatum from other wheat-related species. Thus, K17-1065-4 carrying the stripe rust resistance gene(s) is a novel germplasm useful for breeding disease-resistant wheat cultivars. The molecular markers developed in this study may facilitate the mapping of the stripe rust resistance gene on chromosome 6E of tetraploid Th. elongatum.

Source Acquisition Device Identification from Recorded Audio Based on Spatiotemporal Representation Learning with Multi-Attention Mechanisms.

Source acquisition device identification from recorded audio aims to identify the source recording device by analyzing the intrinsic characteristics of audio, which is a challenging problem in audio forensics. In this paper, we propose a spatiotemporal representation learning framework with multi-attention mechanisms to tackle this problem. In the deep feature extraction stage of recording devices, a two-branch network based on residual dense temporal convolution networks (RD-TCNs) and convolutional neural networks (CNNs) is constructed. The spatial probability distribution features of audio signals are employed as inputs to the branch of the CNN for spatial representation learning, and the temporal spectral features of audio signals are fed into the branch of the RD-TCN network for temporal representation learning. This achieves simultaneous learning of long-term and short-term features to obtain an accurate representation of device-related information. In the spatiotemporal feature fusion stage, three attention mechanisms-temporal, spatial, and branch attention mechanisms-are designed to capture spatiotemporal weights and achieve effective deep feature fusion. The proposed framework achieves state-of-the-art performance on the benchmark CCNU_Mobile dataset, reaching an accuracy of 97.6% for the identification of 45 recording devices, with a significant reduction in training time compared to other models.