Cardiovascular

Global trends and characteristics of metal-organic frameworks in cancer research: a machine-learning-based bibliometric analysis.

BACKGROUND: Cancer poses a significant health threat, causing millions of deaths annually. Although chemotherapy-based comprehensive therapies are common, their low accuracy and severe side effects limit effectiveness. Metal-organic frameworks (MOFs), with their superior biocompatibility and stability, show great promise for drug delivery and cancer treatment. This study aims to explore the potent...

Monte Carlo and Machine Learning-Based Evaluation of Fe-Enriched Al Alloys for Nuclear Radiation Shielding Applications.

This study presents a hybrid computational investigation into the radiation shielding behavior of Fe-enriched Al-based alloys (Al-Fe-Mo-Si-Zr) for potential use in nuclear applications. Four alloy compositions with varying Fe contents (7.21, 6.35, 5.47, and 4.58 wt%) were analyzed using a combination of Monte Carlo simulations, machine learning (ML) predictions based on multilayer perceptrons (MLP...

Development of an immune scoring system based on exosome-related gene expression for prognosis and treatment response prediction in breast cancer.

Breast cancer (BC) is a heterogeneous disease with diverse subtypes that influence prognosis and treatment outcomes. While advances have been made in ...

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...

Spatial discovery of pyrotinib overcoming HER2-positive breast cancer resistance by breaking fibroblast-induced immune barriers.

BACKGROUND: The TCbHP regimen, consisting of combining docetaxel (T), carboplatin (Cb), trastuzumab (H), and pertuzumab (P), is the preferred neoadjuv...

Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer.

INTRODUCTION: A significant challenge in treating non-small cell lung cancer (NSCLC) is its inherent resistance to radiation therapy, leading to poor ...

Comparative evaluation of deep learning-based and conventional reconstruction techniques for image quality enhancement in low-dose chest computed tomography.

BACKGROUND: With the increasing use of computed tomography (CT) in clinical practice, greater attention is being paid to the radiation dose. There are...

Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification.

: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for accurate and efficient diagno...

Identification and validation of hypoxia-associated genes positively correlated with in pulmonary adenocarcinoma.

BACKGROUND: Hypoxia contributes to the proliferation, migration, and chemotherapy resistance of lung adenocarcinoma (LUAD). This study aimed to identi...

Artificial intelligence in breast pathology: Overview and recent updates.

Breast cancer remains a major global health concern where timely and accurate pathologic diagnosis is critical for effective management. The tradition...

[Current status and trends of perioperative immunotherapy for gastric cancer].

In recent years,immunotherapy,particularly immune checkpoint inhibitors,has been widely used in the treatment of both solid and non-solid tumors,and i...

Identification of a novel chemotherapy benefit index for patients with advanced ovarian cancer based on Bayesian network analysis.

BACKGROUND: This study aims to evaluate the efficacy of chemotherapy and optimize treatment strategies for patients with advanced ovarian cancer.

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...

A novel fractional computational neural framework for analyzing cancer model under chemotherapy drug.

In this study, a novel Caputo fractional-order model is proposed to represent the complex interactions among stem cells, effector cells, and tumor cel...

Improved analytical workflow towards machine learning supported N-glycomics-based biomarker discovery.

The composition and function of glycans are very complex thus manual data interpretation of their structural elucidation is difficult. Capillary elect...

Validity and Reliability of an Artificial Intelligence-Based Posture Estimation Software for Measuring Cervical and Lower-Limb Alignment Versus Radiographic Imaging.

: Accurate postural assessment is essential for managing musculoskeletal disorders; however, routine screening is often limited by radiation exposure,...

The development of predictive biomarkers and immunologic markers for breast cancer: current status and future perspectives.

Breast cancer is the leading cause of cancer-related mortality among women worldwide. The development of predictive biomarkers and immunologic markers...

Skeletal Muscle Density as a Predictive Marker for Pathologic Complete Response in Triple-Negative Breast Cancer Treated with Neoadjuvant Chemoimmunotherapy.

: The predictive value of muscle-related indicators in triple-negative breast cancer (TNBC) patients undergoing neoadjuvant chemotherapy (NAC) remains...

National Guidelines for Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy (HIPEC) in Peritoneal Malignancies: A Worldwide Systematic Review and Recommendations of Strength Analysis.

BACKGROUND: National guidelines (GLs) for surgical cytoreduction (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) in the management of peri...

Explainable machine learning and feature interpretation to predict survival outcomes in the treatment of lung cancer.

The treatment outcomes of lung cancer are highly variable, and machine learning (ML) models provide valuable insights into how clinical and biochemica...