The Key Role of Tumor Budding in Predicting the Status of Lymph Node Involvement in Early Gastric Cancer Patients: A Clinical Multicenter Validation in China.

BACKGROUND: Accurate preoperative prediction of lymph node (LN) involvement is essential for the management of early gastric cancer (EGC). Our objective was to formulate a potent nomogram for predicting LN involvement in EGC by leveraging an innovative predictor of tumor budding. METHODS: We assembled a cohort of EGC patients who underwent radical surgery at two tertiary cancer centers. Tumor budding was stratified by using an optimal cutoff value and integrated with other clinicopathological variables to ascertain the risk factors associated with LN involvement. A nomogram was developed and its predictive performance was assessed by using receiver operating characteristic (ROC) curves and calibration plots. In addition, we conducted decision curve analysis to evaluate its clinical utility. Finally, an external validation was conducted by using an independent cohort. RESULTS: Finally, 307 eligible patients (215 in the primary cohort and 92 in the validation cohort) were included. Tumor budding, categorized by a count of two, exhibited a robust association with LN involvement (OR 14.12, p = 0.012). Other significant risk factors include lymphovascular invasion, depth of tumor invasion, ulceration, and tumor differentiation. Notably, the nomogram demonstrated exceptional discriminative power (area under the ROC curve, 0.872 in the primary cohort and 0.885 in the validation cohort) and precise predictive capabilities. Furthermore, the nomogram showed notable clinical applicability through decision curve analysis, particularly in endoscopic curability C-2, by mitigating the risk of overtreatment. CONCLUSIONS: Tumor budding is a robust predictor of LN involvement in EGC. The incorporation of tumor budding into a nomogram is an effective strategy, thereby informing and enhancing clinical decision-making.

EGLN3 attenuates gastric cancer cell malignant characteristics by inhibiting JMJD8/NF-κB signalling activation independent of hydroxylase activity.

BACKGROUND: The expression of Egl-9 family hypoxia-inducible factor 3 (EGLN3) is notably decreased in various malignancies, including gastric cancer (GC). While the predominant focus has been on the hydroxylase activity of EGLN3 for its antitumour effects, recent findings have suggested nonenzymatic roles for EGLN3. METHODS: This study assessed the clinical significance of EGLN3 expression in GC and explored the connection between EGLN3 DNA promoter methylation and transcriptional silencing. To investigate the effect of EGLN3 on GC cells, a gain-of-function strategy was adopted. RNA sequencing was conducted to identify the key effector molecules and signalling pathways associated with EGLN3. RESULTS: EGLN3 expression was significantly reduced in GC tissues, correlating with poorer patient prognosis. EGLN3 hypermethylation disrupts transcriptional equilibrium, contributing to deeper tumour invasion and lymph node metastasis, thus exacerbating GC progression. Conversely, restoration of EGLN3 expression in GC cells substantially inhibited cell proliferation and metastasis. EGLN3 was also found to impede the malignant progression of GC cells by downregulating Jumonji C domain-containing protein 8-mediated activation of the NF-κB pathway, independent of its hydroxylase activity. CONCLUSIONS: EGLN3 has the potential to hinder the spread of GC cells through a nonenzymatic mechanism, thereby shedding light on the complex nature of GC progression.

Development of de-novo coronavirus 3-chymotrypsin-like protease (3CLpro) inhibitors since COVID-19 outbreak: A strategy to tackle challenges of persistent virus infection.

Although no longer a public health emergency of international concern, COVID-19 remains a persistent and critical health concern. The development of effective antiviral drugs could serve as the ultimate piece of the puzzle to curbing this global crisis. 3-chymotrypsin-like protease (3CLpro), with its substrate specificity mirroring that of the main picornavirus 3C protease and conserved across various coronaviruses, emerges as an ideal candidate for broad-spectrum antiviral drug development. Moreover, it holds the potential as a reliable contingency option to combat emerging SARS-CoV-2 variants. In this light, the approved drugs, promising candidates, and de-novo small molecule therapeutics targeting 3CLpro since the COVID-19 outbreak in 2020 are discussed. Emphasizing the significance of diverse structural characteristics in inhibitors, be they peptidomimetic or nonpeptidic, with a shared mission to minimize the risk of cross-resistance. Moreover, the authors propose an innovative optimization strategy for 3CLpro reversible covalent PROTACs, optimizing pharmacodynamics and pharmacokinetics to better prepare for potential future viral outbreaks.

Convolutional Neural Network Defect Detection Algorithm for Wire Bonding X-ray Images.

To address the challenges of complex backgrounds, small defect sizes, and diverse defect types in defect detection of wire bonding X-ray images, this paper proposes a convolutional-neural-network-based defect detection method called YOLO-CSS. This method designs a novel feature extraction network that effectively captures semantic features from different gradient information. It utilizes a self-adaptive weighted multi-scale feature fusion module called SMA which adaptively weights the contribution of detection results based on different scales of feature maps. Simultaneously, skip connections are employed at the bottleneck of the network to ensure the integrity of feature information. Experimental results demonstrate that on the wire bonding X-ray defect image dataset, the proposed algorithm achieves mAP 0.5 and mAP 0.5-0.95 values of 97.3% and 72.1%, respectively, surpassing the YOLO series algorithms. It also exhibits certain advantages in terms of model size and detection speed, effectively balancing detection accuracy and speed.

ATNet: A Defect Detection Framework for X-ray Images of DIP Chip Lead Bonding.

In order to improve the production quality and qualification rate of chips, X-ray nondestructive imaging technology has been widely used in the detection of chip defects, which represents an important part of the quality inspection of products after packaging. However, the current traditional defect detection algorithm cannot meet the demands of high accuracy, fast speed, and real-time chip defect detection in industrial production. Therefore, this paper proposes a new multi-scale feature fusion module (ATSPPF) based on convolutional neural networks, which can more fully extract semantic information at different scales. In addition, based on this module, we design a deep learning model (ATNet) for detecting lead defects in chips. The experimental results show that at 8.2 giga floating point operations (GFLOPs) and 146 frames per second (FPS), mAP0.5 and mAP0.5-0.95 can achieve an average accuracy of 99.4% and 69.3%, respectively, while the detection speed is faster than the baseline yolov5s by nearly 50%.

A Lightweight Method for Detecting IC Wire Bonding Defects in X-ray Images.

Integrated circuit (IC) X-ray wire bonding image inspections are crucial for ensuring the quality of packaged products. However, detecting defects in IC chips can be challenging due to the slow defect detection speed and the high energy consumption of the available models. In this paper, we propose a new convolutional neural network (CNN)-based framework for detecting wire bonding defects in IC chip images. This framework incorporates a Spatial Convolution Attention (SCA) module to integrate multi-scale features and assign adaptive weights to each feature source. We also designed a lightweight network, called the Light and Mobile Network (LMNet), using the SCA module to enhance the framework's practicality in the industry. The experimental results demonstrate that the LMNet achieves a satisfactory balance between performance and consumption. Specifically, the network achieved a mean average precision (mAP50) of 99.2, with 1.5 giga floating-point operations (GFLOPs) and 108.7 frames per second (FPS), in wire bonding defect detection.

Strip Surface Defect Detection Algorithm Based on YOLOv5.

In order to improve the detection accuracy of the surface defect detection of industrial hot rolled strip steel, the advanced technology of deep learning is applied to the surface defect detection of strip steel. In this paper, we propose a framework for strip surface defect detection based on a convolutional neural network (CNN). In particular, we propose a novel multi-scale feature fusion module (ATPF) for integrating multi-scale features and adaptively assigning weights to each feature. This module can extract semantic information at different scales more fully. At the same time, based on this module, we build a deep learning network, CG-Net, that is suitable for strip surface defect detection. The test results showed that it achieved an average accuracy of 75.9 percent (mAP50) in 6.5 giga floating-point operation (GFLOPs) and 105 frames per second (FPS). The detection accuracy improved by 6.3% over the baseline YOLOv5s. Compared with YOLOv5s, the reference quantity and calculation amount were reduced by 67% and 59.5%, respectively. At the same time, we also verify that our model exhibits good generalization performance on the NEU-CLS dataset.

Analysis of Key Genes for Slow Transit Constipation Based on RNA Sequencing.

Purpose: This study aims to identify key genes in slow transit constipation (STC). We also sought to explore the potential link between STC and colorectal cancer. Patients and Methods: mRNA expression profiles were obtained by RNA sequencing, and differentially expressed genes were identified. Functional enrichment analysis and a protein-protein interaction (PPI) network was explored, and differentially expressed genes common to STC and colorectal cancer were examined. Analysis of the effect of constipation and colorectal cancer common genes on the overall survival of colorectal cancer patients based on GEPIA database. Results: Functional enrichment showed that significantly different genes are related to lymphocyte chemotaxis, positive regulation of inflammatory response, cellular response to tumor necrosis factor, extracellular region, extracellular space and chemokine activity. The hub gene for STC was found in the PPI network. In addition, AQP8 and CFD were common differential genes for STC and colorectal cancer. AQP8 affects overall survival in patients with colorectal cancer. Conclusion: Our findings will contribute to understanding the pathology of STC at the molecular level, with the first discovery that AQP8 may be a hub gene in the transition from STC to colorectal cancer.

Study on Filtration Performance of PVDF/PUL Composite Air Filtration Membrane Based on Far-Field Electrospinning.

At present, the situation of air pollution is still serious, and research on air filtration is still crucial. For the nanofiber air filtration membrane, the diameter, porosity, tensile strength, and hydrophilicity of the nanofiber will affect the filtration performance and stability. In this paper, based on the far-field electrospinning process and the performance effect mechanism of the stacked structure fiber membrane, nanofiber membrane was prepared by selecting the environmental protection, degradable and pollution-free natural polysaccharide biopolymer pullulan, and polyvinylidene fluoride polymer with strong hydrophobicity and high impact strength. By combining two kinds of fiber membranes with different fiber diameter and porosity, a three-layer composite nanofiber membrane with better hydrophobicity, higher tensile strength, smaller fiber diameter, and better filtration performance was prepared. Performance characterization showed that this three-layer composite nanofiber membrane had excellent air permeability and filtration efficiency, and the filtration efficiency of particles above PM 2.5 reached 99.9%. This study also provides important reference values for the preparation of high-efficiency composite nanofiber filtration membrane.

[miR-151-3p derived from gastric cancer exosomes induces M2-phenotype polarization of macrophages and promotes tumor growth].

Objective To investigate whether exosomes derived from gastric cancer cells can affect macrophages in tumor microenvironment through miR-151-3p. Methods The expression of miR-151-3p in tumor tissues of patients with gastric cancer and normal tissues was detected by real time quantitative PCR; Gastric cancer cells overexpressing miR-151-3p were constructed, and exosomes were isolated and identified. The expression of CD11b and CD163 markers on RAW264.7 cells co-incubated with exosomes were detected by flow cytometry, and the effects of exosome carrying miR-151-3p on tumor growth and tumor-associated macrophages were evaluated in mice transplanted tumor model. Results The results of real time quantitative PCR showed that the level of miR-151-3p in gastric tumor tissues was significantly higher than that in normal tissues, and the content of miR-151-3p in gastric juice of most patients after operation was lower than that before operation; The content of miR-151-3p in exosomes of tumor cells overexpressing miR-151-3p was also significantly higher than that of untransfected cells. Exosomes carrying miR-151-3p can induce phenotypic differentiation of M2 in co-incubation with RAW264.7 cells. Similarly, tumor transplantation model also showed that exosomes carrying miR-151-3p can induce tumor-associated macrophages to polarize to M2 and promote tumor growth. Conclusion miR-151-3p derived from gastric cancer exosomes can induce the polarization of M2 macrophages and promote the growth of gastric cancer. The treatment of miR-151-3p may destroy the tumor microenvironment of immunosuppression, which assists the anti-tumor immunotherapy.