Ovarian Cancer

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction model for spontaneous rupture bleeding via a machine learning algorithm, and validate and evaluate the predictive efficacy of the model. A retrospective analysis of 4209 patients with hepatocellular carcinoma (HCC) diagnosed at the Second Affiliated Hos...

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types, Hormone Receptor negative (HR-) BRCA cases, especially Basal-like BRCA sub-types, lack estrogen and progesterone hormone receptors and they exhibit a...

Microrobots for Antibiotic-Resistant Skin Colony Eradication.

Self-propelled nano- and micromachines have immense potential as autonomous seek-and-act devices in biomedical applications. In this study, we present...

Split DNA Tetrahedron-Mediated Spatiotemporal-Hierarchy CRISPR Cascade Integrated with Au@Pt Nanolabels and Artificial Intelligence for a Cervical Cancer MicroRNA Bioassay.

The screening and monitoring of microRNAs as cancer molecular biomarkers is clinically significant, but traditional methods lack sufficient sensitivit...

Recent Advances in Fe-Free M-N-C Catalysts for Oxygen Reduction Reaction.

Atomically dispersed metal-nitrogen-carbon (M-N-C) materials, characterized by well-defined coordination structures, have emerged as promising candida...

Pisces: A multi-modal data augmentation approach for drug combination synergy prediction.

Drug combination therapy is promising for cancer treatment by reducing resistance and improving efficacy. Machine learning approaches to predicting dr...

Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer.

BACKGROUND: The most frequent malignant tumor in women is breast cancer (BRCA). It has been discovered that T-cell exhaustion and macrophages play sig...

HR-SC-an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib.

BACKGROUND: High-grade serous ovarian cancer (OC) patients with defects in the homologous recombination repair (HRR) pathway benefit from poly (ADP-ri...

Development of a Transferable Density-Functional Tight-Binding Model for Organic Molecules at the Water/Platinum Interface.

A computationally efficient and transferable approach for modeling reactions at metal/water interfaces could significantly accelerate our understandin...

Correction to "The Melamine-Driven Solvation Effect Promotes Oxygen Reduction on Platinum Catalyst: Machine Learning-Aided Free Energy Calculations".

Ovarian Cancer Detection in Ascites Cytology with Weakly Supervised Model on Nationwide Data Set.

Conventional ascitic fluid cytology for detecting ovarian cancer is limited by its low sensitivity. To address this issue, this multicenter study deve...

Inhibiting Dissolution of Platinum with Atomic Rare Earth Bridged by Nitrogen to Boost Alkaline Hydrogen Evolution.

The unfavorable water dissociation and continuous dissolution of Pt single-atom catalysts significantly impede their practical application in alkaline...

Fullerene Network-Buffered Platinum Nanoparticles Toward Efficient and Stable Electrochemical Ammonia Oxidation Reaction for Hydrogen Production.

Green ammonia is a promising hydrogen carrier due to its well-established production, storage, and transportation infrastructure. Moreover, hydrogen p...

Transforming breast cancer diagnosis and treatment with large language Models: A comprehensive survey.

Breast cancer (BrCa), being one of the most prevalent forms of cancer in women, poses many challenges in the field of treatment and diagnosis due to i...

Machine learning based intratumor heterogeneity related signature for prognosis and drug sensitivity in breast cancer.

Intratumor heterogeneity (ITH) is involved in tumor evolution and drug resistance. Drug sensitivity shows discrepancy in different breast cancer (BRCA...

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification.

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes an...

Addressing Hemolysis-Induced Loss of Sensitivity in Lateral Flow Assays of Blood Samples with Platinum-Coated Gold Nanoparticles and Machine Learning.

Gold nanoparticles (GNPs), which appear red, are widely used as labels in lateral flow assays (LFAs) for visual detection. However, in blood-derived s...

Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.

Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C (HCV) causing approximately 1 million deaths annua...

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy...

Big data analysis and machine learning of the role of cuproptosis-related long non-coding RNAs (CuLncs) in the prognosis and immune landscape of ovarian cancer.

BACKGROUND: Ovarian cancer (OC) is a severe malignant tumor with a significant threat to women's health, characterized by a high mortality rate and po...