Bioinformatics analysis identifies a key gene HLA_DPA1 in severe influenza-associated immune infiltration.

Background: Severe influenza is a serious global health issue that leads to prolonged hospitalization and mortality on a significant scale. The pathogenesis of this infectious disease is poorly understood. Therefore, this study aimed to identify the key genes associated with severe influenza patients necessitating invasive mechanical ventilation.

Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis.

Background: Apoptosis is associated with the pathogenesis of infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI).

Methods: The tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database.

A diagnostic model for distinguishing between active tuberculosis and latent tuberculosis infection based on the blood expression profiles of autophagy-related genes.

Background: Autophagy is closely involved in the control of mycobacterial infection.

Objectives: Here, a diagnostic model was developed using the levels of autophagy-related genes (ARGs) in the blood to differentiate active tuberculosis (ATB) and latent tuberculosis infection (LTBI).

Design: Secondary data analysis of three prospective cohorts.

Adaptive Frequency Learning Network With Anti-Aliasing Complex Convolutions for Colon Diseases Subtypes.

The automatic and dependable identification of colonic disease subtypes by colonoscopy is crucial. Once successful, it will facilitate clinically more in-depth disease staging analysis and the formulation of more tailored treatment plans. However, inter-class confusion and brightness imbalance are major obstacles to colon disease subtyping.

Identification of cuproptosis-related molecular subtypes as a biomarker for differentiating active from latent tuberculosis in children.

Background: Cell death plays a crucial role in the progression of active tuberculosis (ATB) from latent infection (LTBI). Cuproptosis, a novel programmed cell death, has been reported to be associated with the pathology of various diseases. We aimed to identify cuproptosis-related molecular subtypes as biomarkers for distinguishing ATB from LTBI in pediatric patients.

Assessment of the relative benefits of monotherapy and combination therapy approaches to the treatment of hospital-acquired Stenotrophomonas maltophilia pneumonia: a multicenter, observational, real-world study.

Purpose: Stenotrophomonas maltophilia is a Gram-negative pathogen that most commonly causes hospital-acquired infections that can be extremely challenging to treat, contributing to underrecognized mortality throughout the world. The relative benefits of monotherapy as compared to combination therapy in patients diagnosed with S. maltophilia pneumonia, however, have yet to be established.

Genetic analysis of cuproptosis subtypes and immunological features in severe influenza.

The mechanisms regulating cuproptosis in severe influenza are still unknown. We aimed to identify the molecular subtypes of cuproptosis and immunological characteristics associated with severe influenza in patients requiring invasive mechanical ventilation (IMV). The expression of cuproptosis modulatory factors and immunological characteristics of these patients were analyzed using the public datasets (GSE101702, GSE21802, and GSE111368) from the Gene Expression Omnibus (GEO).

Assessment of ferroptosis-associated gene signatures as potential biomarkers for differentiating latent from active tuberculosis in children.

Ferroptotic cell death is a regulated process that is governed by iron-dependent membrane lipid peroxide accumulation that plays a pathogenic role in several disease-related settings. The use of ferroptosis-related genes (FRGs) to distinguish active tuberculosis (ATB) from latent tuberculosis infection (LTBI) among children, however, remains to be analysed. Tuberculosis-related gene expression data and FRG lists were obtained, respectively, from Gene Expression Omnibus (GEO) and FerrDb.

DLGNet: A dual-branch lesion-aware network with the supervised Gaussian Mixture model for colon lesions classification in colonoscopy images.

Colorectal cancer is one of the malignant tumors with the highest mortality due to the lack of obvious early symptoms. It is usually in the advanced stage when it is discovered. Thus the automatic and accurate classification of early colon lesions is of great significance for clinically estimating the status of colon lesions and formulating appropriate diagnostic programs.

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis.

Methods: Three Gene Expression Omnibus (GEO) microarray datasets (GSE19491, GSE98461, and GSE152532) were downloaded, with GSE19491 and GSE98461 then being merged to form a training dataset. Hub genes capable of differentiating between ATB and LTBI were then identified through differential expression analyses and a WGCNA analysis of this training dataset. Receiver operating characteristic (ROC) curves were then used to gauge to the diagnostic accuracy of these hub genes in the test dataset (GSE152532).

Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation.

Background: Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV).

Application of endoscopic ultrasonography for detecting esophageal lesions based on convolutional neural network.

Background: A convolutional neural network (CNN) is a deep learning algorithm based on the principle of human brain visual cortex processing and image recognition.

Aim: To automatically identify the invasion depth and origin of esophageal lesions based on a CNN.

Methods: A total of 1670 white-light images were used to train and validate the CNN system.

Acetyl-11-keto-β-boswellic acid inhibits proliferation and induces apoptosis of gastric cancer cells through the phosphatase and tensin homolog /Akt/ cyclooxygenase-2 signaling pathway.

Background: Gastric cancer is one of the most common malignant tumors of the digestive system worldwide, posing a serious danger to human health. Cyclooxygenase (COX)-2 plays an important role in the carcinogenesis and progression of gastric cancer. Acetyl-11-keto-β-boswellic acid (AKBA) is a promising drug for cancer therapy, but its effects and mechanism of action on human gastric cancer remain unclear.

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.

Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the esophageal diseases and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors, and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs).

Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network.

Background: Using deep learning techniques in image analysis is a dynamically emerging field. This study aims to use a convolutional neural network (CNN), a deep learning approach, to automatically classify esophageal cancer (EC) and distinguish it from premalignant lesions.

Methods: A total of 1,272 white-light images were adopted from 748 subjects, including normal cases, premalignant lesions, and cancerous lesions; 1,017 images were used to train the CNN, and another 255 images were examined to evaluate the CNN architecture.

Myrrh induces the apoptosis and inhibits the proliferation and migration of gastric cancer cells through down-regulating cyclooxygenase-2 expression.

Objective: The present study is designed to evaluate the anti-tumor effects of myrrh on human gastric cancer both in vitro and in vivo.

Methods: The gastric cancer cell proliferation was determined by MTT assay. Apoptosis was measured by flow cytometry and Hoechst 33342 staining.

A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images.

Tracking the myotendinous junction (MTJ) in consecutive ultrasound images is crucial for understanding the mechanics and pathological conditions of the muscle-tendon unit. However, the lack of reliable and efficient identification of MTJ due to poor image quality and boundary ambiguity restricts its application in motion analysis. In recent years, with the rapid development of deep learning, the region-based convolution neural network (RCNN) has shown great potential in the field of simultaneous objection detection and instance segmentation in medical images.

Pre-clinical characterization of PKC412, a multi-kinase inhibitor, against colorectal cancer cells.

The potential effect of PKC412, a small molecular multi-kinase inhibitor, in colorectal cancer (CRC) cells was evaluated here. We showed that PKC412 was cytotoxic and anti-proliferative against CRC cell lines (HT-29, HCT-116, HT-15 and DLD-1) and primary CRC cells. PKC412 provoked caspase-dependent apoptotic death, and induced G2-M arrest in the CRC cells.

Gastrin acting on the cholecystokinin2 receptor induces cyclooxygenase-2 expression through JAK2/STAT3/PI3K/Akt pathway in human gastric cancer cells.

Gastrin, cholecystokinin2 receptor (CCK2R), and cyclooxygenase-2 (COX-2) have been implicated in the carcinogenesis and progression of gastric cancer. Our study demonstrated that antagonist or siRNA against CCK2R blocked amidated gastrin (G17)-induced activation of STAT3 and Akt in gastric cancer cell lines. G17-increased COX-2 expression and cell proliferation were effectively blocked by CCK2R antagonist and inhibitors of JAK2 and PI3K.

Downregulation of miR-101 in gastric cancer correlates with cyclooxygenase-2 overexpression and tumor growth.

Cyclooxygenase-2 (COX-2) plays an important role in the carcinogenesis and progression of gastric cancer. It has been demonstrated that COX-2 overexpression depends on different cellular pathways, involving both transcriptional and post-transcriptional regulation. MicroRNAs (miRNAs) are small, noncoding RNAs that function as post-transcriptional regulators.