Oncogenic KRAS Promotes Ferroptosis in Pancreatic Cancer Through Regulation of the Fosl1-Tfrc Axis.

Mutant KRAS activation occurs in most of pancreatic ductal adenocarcinoma (PDAC), which induce the sensitivity to ferroptosis of PDAC cells, but the underlying mechanism is still poorly understood. Here, we show how KRAS acts in signaling to activate transcription factor FOSL1, which promotes the expression of the iron uptake receptor TFRC. In PDAC cells, repression of TFRC by KRAS/FOSL1 signaling inhibited intracellular iron levels, thereby restricting the occurrence of ferroptosis.

Oncocytic cell carcinoma of the thyroid with TERT promoter mutation presenting as asphyxia in an elderly: a case report.

Introduction: The prevalence of thyroid nodules and malignancies in the elderly is a growing concern. Thyroid nodules in this population have unique characteristics, requiring careful treatment strategies that balance risks and benefits. Oncocytic carcinoma of the thyroid (OCA) is a rare, aggressive subtype with diagnostic challenges.

ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas.

The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnostic markers. We conducted a comprehensive analysis of bulk RNA sequencing data from SPNs, NETs, and normal pancreas, followed by experimental validation. This analysis revealed an increased accumulation of peroxisomes in SPNs.

A nonmetallic plasmonic catalyst for photothermal CO flow conversion with high activity, selectivity and durability.

The meticulous design of active sites and light absorbers holds the key to the development of high-performance photothermal catalysts for CO hydrogenation. Here, we report a nonmetallic plasmonic catalyst of MoN/MoO-x nanosheets by integrating a localized surface plasmon resonance effect with two distinct types of active sites for CO hydrogenation. Leveraging the synergism of dual active sites, H and CO molecules can be simultaneously adsorbed and activated on N atom and O vacancy, respectively.

Key genes involved with prognosis were identified in lung adenocarcinoma by integrated bioinformatics analysis.

Objective: By screening the core genes in lung adenocarcinoma (LUAD) with bioinformatics, our study evaluated its prognosis value and role in infiltration process of immune cells.

Methods: Using GEO database, we screened 5 gene chips, including GSE11072, GSE32863, GSE43458, GSE115002, and GSE116959. Then, we obtained the corresponding differentially expressed genes by analyzed 5 gene chips online by GEO2R (P < 0.

A partial convolution generative adversarial network for lesion synthesis and enhanced liver tumor segmentation.

Lesion segmentation is critical for clinicians to accurately stage the disease and determine treatment strategy. Deep learning based automatic segmentation can improve both the segmentation efficiency and accuracy. However, training a robust deep learning segmentation model requires sufficient training examples with sufficient diversity in lesion location and lesion size.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics.

Objective: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment.

Methods: Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.

Label-free coronavirus disease 2019 lesion segmentation based on synthetic healthy lung image subtraction.

Purpose: Coronavirus disease 2019 (COVID-19) has become a global pandemic and is still posing a severe health risk to the public. Accurate and efficient segmentation of pneumonia lesions in computed tomography (CT) scans is vital for treatment decision-making. We proposed a novel unsupervised approach using a cycle consistent generative adversarial network (cycle-GAN) which automates and accelerates the process of lesion delineation.