Cardiovascular

Computational methods, clinical evidence, and translational readiness for artificial intelligence for HER2 assessment using silver in situ hybridization (SISH).

Accurate determination of human epidermal growth factor receptor 2 (HER2) gene amplification is a cornerstone of precision oncology in breast cancer and an expanding range of solid tumors. While immunohistochemistry (IHC) remains the first-line screening modality, in situ hybridization (ISH) techniques are essential for resolving equivocal cases and providing quantitative assessment of HER2 gene s...

[Paeoniflorin alleviates cancer-related fatigue during chemotherapy for breast cancer by targeting EZH2/CCNE1 to regulate cell cycle and inflammatory microenvironment].

OBJECTIVES: To explore the molecular mechanisms and key pharmacodynamic basis of Baishao for alleviating cancer-related fatigue (CRF) during chemotherapy for breast cancer. METHODS: Bioinformatics analyses were used to explore the active ingredients and potential targets of Baishao, CRF-related targets, and the differentially expressed core genes between chemotherapy and non-chemotherapy groups. F...

[Reflections on surgical management strategies for locally advanced gastric cancer after neoadjuvant immunotherapy].

Neoadjuvant immunotherapy is reshaping the treatment landscape of locally advanced gastric cancer (GC). The central clinical challenge has expanded fr...

Artificial intelligence-driven prediction of neoadjuvant chemotherapy response in adult patients with gastric cancer: a systematic review and meta-analysis.

OBJECTIVES: A substantial proportion of adults with locally advanced gastric cancer derive limited benefit from neoadjuvant chemotherapy (NAC), with a...

Radiomics using generative adversarial network enhanced non-contrast computed tomography for gastric cancer diagnosis.

Objective.Non-contrast-enhanced computed tomography (NCCT) images have limited tissue resolution for gastric cancer diagnosis, while contrast-enhanced...

Global trends and emerging frontiers in robot-assisted vascular intervention: a comprehensive bibliometric analysis.

Robot-assisted vascular intervention may reduce occupational radiation exposure, improve procedural stability, and expand access to specialized endova...

Nanotechnology-based immunotherapy: integrating Artificial Intelligence (AI) with current strategies in combating brain cancer disease.

Brain cancer is one of the most challenging malignancies and a major contributor to worldwide morbidity and mortality. Glioblastoma, the most aggressi...

Prediction of Side Effects from Breast Radiation Therapy - Integration of Clinical and Genomic Data.

OBJECTIVES: To compare the ability of different machine learning models to predict the risk of side effects in patients with breast cancer undergoing ...

Multimodal Artificial Intelligence System for Risk-Adapted Cancer Survivorship Surveillance: A Multicenter Target Trial Emulation.

BACKGROUND: The growing population of cancer survivors faces immense monitoring burdens due to rigid follow-up guidelines, such as the intensive surve...

Integrating Cardiovascular Screening in Lung Cancer Screening.

Cardiovascular risk assessment is a natural extension of lung cancer screening (LCS) because individuals eligible for low-dose computed tomography oft...

Next generation graphene based MXene metamaterial multicylinder resonator solar absorber optimized with machine learning for conceptual solar dryer mining applications.

In the field of industry and mining sectors, energy demand is rapidly increasing, and it is very necessary to develop a durable, scalable, and broadba...

Transcriptomic Profiling of Mouse Mammary Tumors Enables Prognostic and Predictive Biomarker Discovery for Human Breast Cancer.

The development and validation of prognostic and predictive biomarkers in breast cancer is limited by the availability of well-annotated datasets link...

From optimization of radiation dose distributions to optimal cancer treatments.

The history of optimization in radiation oncology is closely intertwined with the history of Physics in Medicine & Biology (PMB). Over the past four d...

Modeling microbiome modulation of tumor metabolic networks to predict synergistic therapies.

Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic impact of microbes on host cells and ther...

Dosimetric parameter-based machine learning to predict radiation pneumonitis following breast radiotherapy.

Objective.Radiation pneumonitis (RP) is an important toxicity following breast radiotherapy. Although modern treatment techniques limit lung exposure,...

Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns.

BACKGROUND: Liver enzyme biomarkers are known to contribute to the onset and progression of colorectal cancer. OBJECTIVE: To develop a novel oxidative...

Validation of tumour-negative lymph node size as a novel independent prognostic biomarker in oesophagogastric adenocarcinoma: post hoc analysis of the UK MRC OE05 and ST03 randomised trials.

BACKGROUND: The size of tumour-negative regional lymph nodes (LNnegs) may be associated with the host antitumour immune response in patients with oeso...

Biologically adaptive radiation oncology in oligometastatic and oligoprogressive disease: MR- and CBCT-guided adaptation, molecular imaging, dose painting, biomarkers, and AI.

Oligometastatic disease (OMD) and oligoprogression are clinically actionable states in which local treatment of limited tumour burden can prolong dise...

From Andes to the dry diagonal: phylogenomics and trait evolution elucidate the radiation of cacti in South American drylands.

The expansion of arid and dry ecosystems during the Miocene played a key role in the diversification of succulent plants; however, the relative contri...

Deep learning for CT-less attenuation correction synthesis in non-small cell lung cancer: A proof-of-concept evaluated for quantitative and physical characteristics.

To develop and rigorously validate a deep learning framework for CT-free positron emission tomography (PET) attenuation correction in non-small cell l...