Follow-up analysis and research of very low-risk and low-risk gastrointestinal stromal tumors after endoscopic resection.

Currently, due to limited long-term evidence, there remains some controversy surrounding the recommended postoperative monitoring strategy for primary low-risk gastrointestinal stromal tumors (GISTs). This study recruited a total of 532 patients diagnosed with very low-risk and low-risk GISTs who underwent endoscopic resection from 2015 to 2021, including 460 very low-risk patients and 72 low-risk patients. Descriptive statistical analysis was used to evaluate the clinical and pathological characteristics of GIST patients, and Kaplan-Meier methods were employed for survival analysis. The results showed that the 5-year recurrence-free survival rates for very low-risk and low-risk patients were 98.5% and 95.9%, respectively. The 5-year disease-specific survival rates for both groups were 100%. Additionally, the 5-year overall survival rates were 99.7% for very low-risk patients and 100% for low-risk patients (P = 0.69). Therefore, it is suggested that routine follow-up monitoring, including endoscopic surveillance and imaging, may not be necessary for very low-risk and low-risk GISTs after endoscopic resection.

Introgression of tetraploid Thinopyrum elongatum 6EL segments enhances the stripe rust resistance of adult wheat plants.

Preventing the widespread occurrence of stripe rust in wheat largely depends on the identification of new stripe rust resistance genes and the breeding of cultivars with durable resistance. In previous study, we reported 6E of wheat-tetraploid Thinopyrum elongatum 6E (6D) substitution line contains adult-stage stripe rust resistance genes. In this study, three novel wheat-tetraploid Th. elongatum translocation lines were generated from the offspring of a cross between common wheat and the 6E (6D) substitution line. Genomic in situ hybridization (GISH), fluorescence in situ hybridization chromosome painting (FISH painting), repetitive sequential FISH, and 55 K SNP analyses indicated that K227-48, K242-82, and K246-6 contained 42 chromosomes and were 6DL·6ES, 2DL·6EL, and 6DS·6EL translocation lines, respectively. The assessment of stripe rust resistance revealed that K227-48 was susceptible to a mixture of Pst races, whereas the 6EL lines K242-82 and K246-6 were highly resistance to stripe rust at the adult stage. Thus, this resistance was due to the chromosome arm 6EL of tetraploid Th. elongatum. The improved agronomic performance of 6DS·6EL translocation line may be a useful novel germplasm resource for wheat breeding programs. For the application of marker-assisted selection (MAS), 47 simple sequence repeat (SSR) markers were developed, showing specific amplification on the chromosome 6E using the whole-genome sequence of diploid Th. elongatum. The 6DS·6EL translocation line and SSR markers have the potential to be deploy for future stripe rust resistance wheat breeding program. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-024-01493-6.

Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma.

OBJECTIVES: Radiomics has been demonstrated to be strongly associated with TNM stage and patient prognosis. We aimed to develop a model for predicting lymph node metastasis (LNM) and survival. METHODS: For radiomics texture selection, 3D Slicer 5.0.3 software and the least absolute shrinkage and selection operator (LASSO) algorithm were used. Subsequently, the radiomics model, computed tomography (CT) image, and clinical risk model were compared. The performance of the three models was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), calibration plots, and clinical impact curves (CICs). RESULTS: For the LNM prediction model, 224 patients with LNM information were used to construct a model that was applied to predict LNM. According to the CT data and clinical characteristics, we constructed a radiomics model, CT imaging model and clinical model. The radiomics model for evaluating LNM status showed excellent calibration and discrimination in the training cohort (AUC = 0.926, 95% CI = 0.869-0.982) and the validation cohort (AUC = 0.872, 95% CI = 0.802-0.941). DeLong's test demonstrated that the difference among the three models was significant. Similarly, DCA and CIC showed that the radiomics model has better clinical utility than the CT imaging model and clinical model. Our model also exhibited good performance in predicting survival-in line with the findings of the model built with clinical risk factors. CONCLUSIONS: CT radiomics models exhibited better predictive performance for LNM than models built based on clinical risk characteristics and CT imaging and had comparative clinical utility for predicting patient prognosis. CRITICAL RELEVANCE STATEMENT: The radiomics model showed excellent performance and discrimination for predicting LNM and survival of duodenal papillary carcinoma (DPC). KEY POINTS: LNM status determines the most appropriate treatment for DPC. Our radiomics model for evaluating the LNM status of DPC performed excellently. The radiomics model had high sensitivity and specificity for predicting survival, exhibiting great clinical value.

Causal effects of circulating glutamine on colitis, IBD, and digestive system cancers: a Mendelian randomisation study.

Backgrounds: There is growing evidence linking glutamine levels to the risk of gastrointestinal diseases, yet the presence of a causal relationship remains uncertain. In this study, we employed a Mendelian randomization (MR) approach to investigate potential causal associations between glutamine and colitis, inflammatory bowel disease (IBD), and digestive tumors. Methods: Genetic instrumental variables for glutamine exposure were identified from a genome-wide association study (GWAS) involving 114,751 participants. We pooled statistics from GWAS of gastrointestinal diseases in European populations, encompassing colitis (cases=1193, controls=461,740), IBD (cases=31,665, controls=33,977), Crohn's disease (cases=17,897, controls=33,977), ulcerative colitis (cases=1,239, controls=990), oesophageal cancer (cases=740, controls=372,016), gastric cancer (cases=6,563, controls=195,745), liver cell carcinoma (cases=168, controls=372,016), hepatic bile duct cancer (cases=418, controls=159,201), pancreatic cancer (cases=1,196, controls=475,049), and colon cancer (cases=1,494, controls=461,439). To ensure the validity of our findings, we utilized several analytical approaches including inverse variance weighted, weighted median, weighted mode, MR-Egger, and simple mode method. Results: Using the IVW method, we found that glutamine levels were inversely associated with colon cancer (OR = 0.998; 95% CI: 0.997-1.000; P = 0.027), colitis (OR = 0.998; 95% CI: 0.997-1.000; P = 0.020), and IBD (OR = 0.551; 95% CI: 0.343-0.886; P = 0.014). Subgroup analysis revealed a negative association between glutamine and Crohn's disease (OR = 0.375; 95% CI: 0.253-0.557; P = 1.11E-06), but not with ulcerative colitis (OR = 0.508; 95% CI: 0.163-1.586; P = 0.244). Glutamine levels showed no significant correlation with oesophageal cancer (OR = 1.000; 95% CI: 0.999-1.001; P = 0.566), gastric cancer (OR = 0.966; 95% CI: 0.832-1.121; P = 0.648), liver cell carcinoma (OR = 1.000; 95% CI: 0.999-1.000; P = 0.397), hepatic bile duct cancer (OR = 0.819; 95% CI: 0.499-1.344; P = 0.430), and pancreatic cancer (OR = 1.130; 95% CI: 0.897-1.423; P = 0.301). Sensitivity analyses also supports this finding, affirming the reliability and robustness of our study. Conclusions: This study suggests that blood glutamine levels in European populations may lower the risk of colon cancer, colitis, and IBD, particularly Crohn's disease. Nevertheless, additional research involving a diverse range of ancestries is imperative to corroborate this causal relationship.

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.

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.

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.

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.

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.

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.

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.

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.

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.