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Predicting Survival Rates in Brain Metastases Patients from Non-Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity.
Adv Sci (Weinh)
January 2025
The department of oncology, Xiangya Hospital, Central South University, Changsha, 410008, China.
Non-small cell lung cancer (NSCLC) frequently metastasizes to the brain, significantly worsened prognoses. This study aimed to develop an interpretable model for predicting survival in NSCLC patients with brain metastases (BM) integrating radiomic features and RNA sequencing data. 292 samples are collected and analyzed utilizing T1/T2 MRIs.
View Article and Find Full Text PDFComprehensive analysis of adverse events associated with onasemnogene abeparvovec (Zolgensma) in spinal muscular atrophy patients: insights from FAERS database.
Front Pharmacol
January 2025
Department of Fetal Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Onasemnogene Abeparvovec (Zolgensma) is a gene therapy for the treatment of Spinal Muscular Atrophy (SMA) with improved motor neuron function and the potential for a singular treatment. Information on its adverse drug reactions is mainly from clinical trials and real-world studies with extensive sample sizes are lacking. In this study, we analyzed the U.
View Article and Find Full Text PDFExploring the shared gene signatures and mechanism among three autoimmune diseases by bulk RNA sequencing integrated with single-cell RNA sequencing analysis.
Front Mol Biosci
January 2025
Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Background: Emerging evidence underscores the comorbidity mechanisms among autoimmune diseases (AIDs), with innovative technologies such as single-cell RNA sequencing (scRNA-seq) significantly advancing the explorations in this field. This study aimed to investigate the shared genes among three AIDs-Multiple Sclerosis (MS), Systemic Lupus Erythematosus (SLE), and Rheumatoid Arthritis (RA) using bioinformatics databases, and to identify potential biomarkers for early diagnosis.
Methods: We retrieved transcriptomic data of MS, SLE, and RA patients from public databases.
Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy.
Front Immunol
January 2025
Department of Radiation Oncology, Lianyungang Second People's Hospital (Lianyungang Tumur Hospital), Lianyungang, China.
Background: Hepatocellular carcinoma (LIHC) poses a significant health challenge worldwide, primarily due to late-stage diagnosis and the limited effectiveness of current therapies. Cancer stem cells are known to play a role in tumor development, metastasis, and resistance to treatment. A thorough understanding of genes associated with stem cells is crucial for improving the diagnostic precision of LIHC and for the advancement of effective immunotherapy approaches.
View Article and Find Full Text PDFObjective: To assist in the rapid clinical identification of brain tumor types while achieving segmentation detection, this study investigates the feasibility of applying the deep learning YOLOv5s algorithm model to the segmentation of brain tumor magnetic resonance images and optimizes and upgrades it on this basis.
Methods: The research institute utilized two public datasets of meningioma and glioma magnetic resonance imaging from Kaggle. Dataset 1 contains a total of 3,223 images, and Dataset 2 contains 216 images.
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