Identification of pivotal genes and crucial pathways in liver fibrosis through WGCNA analysis.

Background: Liver fibrosis is a progressive liver disease with increasing incidence, yet its underlying pathogenic mechanisms remain incompletely understood.

Objective: : This study aims to explore potential therapeutic targets for liver fibrosis using weighted gene co-expression network analysis (WGCNA) and experimental validation.

Methods: We retrieved the microarray data (GSE174099) from the GEO database and performed differential expression analysis and WGCNA to identify co-expression modules associated with liver fibrosis.

Deep learning-based and BI-RADS guided radiomics model for automatic tumor-infiltrating lymphocytes evaluation in breast cancer.

Objectives: To investigate an interpretable radiomics model consistent with clinical decision-making process and realize automatic prediction of tumor-infiltrating lymphocytes (TILs) levels in breast cancer (BC) from ultrasound (US) images.

Methods: A total of 378 patients with invasive BC confirmed by pathological results were retrospectively enrolled in this study. Radiomics features were extracted guided by the BI-RADS lexicon from the regions of interest(ROIs) segmented with deep learning models.

MFMSNet: A Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network for breast ultrasound image segmentation.

Breast tumor segmentation in ultrasound images is fundamental for quantitative analysis and plays a crucial role in the diagnosis and treatment of breast cancer. Recently, existing methods have mainly focused on spatial domain implementations, with less attention to the frequency domain. In this paper, we propose a Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network (MFMSNet).

The value of Demetics ultrasound-assisted diagnosis system in diagnosis of breast lesions and in assessment Ki-67 status of breast cancer.

Purpose: This study aims to explore the diagnostic efficiency of the Demetics for breast lesions and assessment of Ki-67 status.

Material: This retrospective study included 291 patients. Three combined methods (method 1: upgraded BI-RADS when Demetics classified the breast lesion as malignant; method 2: downgraded BI-RADS when Demetics classified the breast lesion as benign; method 3: BI-RADS was upgraded or downgraded according to Demetrics' diagnosis) were used to compare the diagnostic efficiency of two radiologists with different seniority before and after using Demetics.

Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network.

Objective: Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images.

Methylated SEPT9 combined with AFP and PIVKA-II is effective for the detection of HCC in high-risk population.

Background: The methylation SEPT9 (mSEPT9) appeared to be effective for hepatocellular carcinoma (HCC) detection. However, its performance in high-risk population has not been validated. We designed a pilot study and aimed to investigate the performance of mSEPT9, AFP, PIVKA-II and their combination in hepatic cirrhosis (HC) population.

Deep Learning with Transformer or Convolutional Neural Network in the Assessment of Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Based on US Images: A Dual-Center Retrospective Study.

This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. Of these, 396 patients from hospital 1 were divided into the training cohort ( = 298) and internal validation (IV) cohort ( = 98).

Analysis of angiogenesis-related subtypes of hepatocellular carcinoma and tumor microenvironment infiltration feature in hepatocellular carcinoma.

Purpose: Hepatocellular carcinoma (HCC) is a highly vascularized tumor, and angiogenesis plays an important role in its progression. However, the role of angiogenesis in cell infiltration in the tumor microenvironment (TME) remains unclear.

Methods: We evaluated the associations of 35 angiogenesis-related genes (ARGs) with the clinicopathological features of 816 HCC patients.

Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Hepatocellular Carcinoma.

Background: Liver cancer is a major medical problem because of its high morbidity and mortality. Hepatocellular carcinoma (HCC) is the most common type of liver cancer. Currently, the mechanism of HCC is unclear, and the prognosis is poor with limited treatment.

Prolonged warm ischemia aggravates hepatic mitochondria damage and apoptosis in DCD liver by regulating Ca/CaM/CaMKII signaling pathway.

This study was conducted to investigate the effect of warm ischemia duration on hepatocyte mitochondrial damage after liver transplantation, and confirm the role of CaMKIIγ in this process. Rat donation after cardiac death (DCD) liver transplantation model was established by exposing donor liver to 0 (W group), 15 (W group), and 30 (W group) min warm ischemia. Some rats in W group were transfected with CaMKIIγ and CaMKIIγ-shRNA lentivirus.