Cardiovascular

Machine learning-assisted high-throughput prediction and experimental validation of high-responsivity extreme ultraviolet detectors.

Identifying materials with optimal optoelectronic properties for targeted applications represents both a critical need and a persistent challenge in optoelectronic device engineering. Machine learning models often depend on extensive datasets, which are typically lacking in specialized research domains such as extreme ultraviolet (EUV) radiation detection. Here, we demonstrate a Cross-Spectral Res...

Interpretable machine learning models for survival prediction in prostate cancer bone metastases.

Prostate cancer bone metastasis (PCBM) is a highly lethal condition with limited survival. Accurate survival prediction is essential for managing these typically incurable patients. However, existing clinical models lack precision. This study seeks to establish machine learning models to improve survival predictions for PCBM patients. We extracted data for PCBM patients from the SEER database span...

A computational study of cardiac glycosides from Vernonia amygdalina as PI3K inhibitors for targeting HER2 positive breast cancer.

The PI3K/Akt pathway plays a crucial role in regulating a broad network of proteins involved in the proliferation of HER2-positive breast cancer. The ...

Machine learning models for predicting chemotherapy-induced adverse drug reactions in colorectal cancer patients.

BACKGROUND: Chemotherapy-induced adverse drug reactions (ADRs) are common in patients with colorectal cancer. We developed four machine learning model...

Robot-Assisted Retrograde Intrarenal Surgery: A Mini Review of the Literature on Robotic Surgery Platforms for Endoscopic Stone Surgery.

Retrograde intrarenal surgery (RIRS) has become a cornerstone in renal stone management, with robotic platforms recently entering clinical practice. T...

Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex mo...

Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis.

This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning ...

Precise metabolic dependencies of cancer through deep learning and validations.

Cancer cells exhibit metabolic reprogramming to sustain proliferation, creating metabolic vulnerabilities absent in normal cells. While prior studies ...

Sparse coding-based multiframe superresolution for efficient synchrotron radiation microspectroscopy.

In nanostructure extraction, advanced techniques like synchrotron radiation and electron microscopy are often hindered by radiation damage and chargin...

Intelligent assistant in radiation protection based on large language model with knowledge base.

Radiation protection is a critical pillar supporting the use of nuclear energy and nuclear technologies. The radiation protection system has been esta...

Cohesive data analysis for the identification of prognostic hub genes and significant pathways associated with HER2 + and TN breast cancer types.

Breast cancer is the most prevalent and lethal form of cancer being the utmost common medical concern of women. Breast cancer etiology implicates nume...

Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiologic...

Predicting cisplatin response in cholangiocarcinoma patients using chromosome pattern and related gene expression.

Cholangiocarcinoma (CCA) is a prevalent bile duct cancer with limited treatment options. Cisplatin-based chemotherapy is a common approach, but respon...

Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides.

Accurate risk stratification is critical for guiding treatment decisions in early breast cancer. We present an artificial intelligence (AI)-based tool...

Machine learning developed LKB1-AMPK signaling related signature for prognosis and drug sensitivity in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide, posing a significant threat to the life and health of people globally. LKB1...

Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps.

This study integrates multimodal metabolomic data from three platforms-LC-MS, GC-MS, and NMR-to systematically identify biomarkers distinguishing brea...

Clinical characteristics and predictive models of HER2-low breast cancer patients who only received adjuvant chemotherapy: a real-world retrospective multicenter study.

HER2-low positivity is reported to play substantial role in neoadjuvant chemotherapy for breast cancer, but its role in adjuvant chemotherapy remains ...

Auto-Segmentation via deep-learning approaches for the assessment of flap volume after reconstructive surgery or radiotherapy in head and neck cancer.

Reconstructive flap surgery aims to restore the substance and function losses associated with tumor resection. Automatic flap segmentation could allow...

Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.

Some studies have developed machine learning (ML) models for the prediction of pneumonitis following immunotherapy and radiotherapy for patients with ...

Evaluation of MRI-based synthetic CT for lumbar degenerative disease: a comparison with CT.

Patients with lumbar degenerative disease typically undergo preoperative MRI combined with CT scans, but this approach introduces additional ionizing ...