Application of the convolution neural network in determining the depth of invasion of gastrointestinal cancer: a systematic review and meta-analysis.

Background: With the development of endoscopic technology, endoscopic submucosal dissection (ESD) has been widely used in the treatment of gastrointestinal tumors. It is necessary to evaluate the depth of tumor invasion before the application of ESD. The convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist in the classification of the depth of invasion in endoscopic images.

A Deep Learning Application of Capsule Endoscopic Gastric Structure Recognition Based on a Transformer Model.

Background: Gastric structure recognition systems have become increasingly necessary for the accurate diagnosis of gastric lesions in capsule endoscopy. Deep learning, especially using transformer models, has shown great potential in the recognition of gastrointestinal (GI) images according to self-attention. This study aims to establish an identification model of capsule endoscopy gastric structures to improve the clinical applicability of deep learning to endoscopic image recognition.

RUNX1 promotes angiogenesis in colorectal cancer by regulating the crosstalk between tumor cells and tumor associated macrophages.

Colorectal cancer (CRC) is a common malignancy worldwide. Angiogenesis and metastasis are the critical hallmarks of malignant tumor. Runt-related transcription factor 1 (RUNX1), an efficient transcription factor, facilitates CRC proliferation, metastasis and chemotherapy resistance.

DDX39B contributes to the proliferation of colorectal cancer through direct binding to CDK6/CCND1.

DDX39B (also called UAP56 or BAT1) which is a kind of DEAD-box family helicase plays pivotal roles in mRNA binding, splicing, and export. It has been found upregulated in many kinds of tumors as an oncogene. Nevertheless, the underlying molecular mechanisms of DDX39B in the proliferation of human colorectal cancer (CRC) remain fairly elusive.

Identification of Immunological Biomarkers of Atopic Dermatitis by Integrated Analysis to Determine Molecular Targets for Diagnosis and Therapy.

Purpose: Atopic dermatitis (AD) is a common chronic inflammatory skin disorder associated with immune dysregulation and barrier dysfunction. In this study, we investigated immunological biomarkers for AD diagnosis and treatment using CIBERSORT to identify immune cell infiltration characteristics.

Patients And Methods: Common differentially expressed genes (DEGs) of lesioned (LS) vs non-lesioned (NL) groups were obtained from public datasets (GSE140684 and GSE99802).

Convolution neural network for the diagnosis of wireless capsule endoscopy: a systematic review and meta-analysis.

Background: Wireless capsule endoscopy (WCE) is considered to be a powerful instrument for the diagnosis of intestine diseases. Convolution neural network (CNN) is a type of artificial intelligence that has the potential to assist the detection of WCE images. We aimed to perform a systematic review of the current research progress to the CNN application in WCE.

Role of the Receptor for Advanced Glycation End Products in Heat Stress-Induced Endothelial Hyperpermeability in Acute Lung Injury.

Objective: To study the role of the receptor for advanced glycation end products (RAGE) in endothelial barrier dysfunction induced by heat stress, to further explore the signal pathway by which RAGE contributes to heat-induced endothelia response, and thereby find a novel target for the clinical treatment of ALI (acute lung injury) induced by heatstroke.

Methods: This study established the animal model of heatstroke using RAGE knockout mice. We observed the role of RAGE in acute lung injury induced by heatstroke in mice by evaluating the leukocytes, neutrophils, and protein concentration in BALF (Bronchoalveolar lavage fluids), lung wet/dry ratio, histopathological changes, and the morphological ultrastructure of lung tissue and arterial blood gas analysis.