Oncology/Hematology

Advanced intelligent drug-device combination systems: regulatory landscapes, technological foundations, and clinical translation.

Drug-device combinations (DDCs) have evolved from simple drug-coated implants into sophisticated intelligent platforms capable of real-time sensing, adaptive drug delivery, and closed-loop therapeutic control. This transformation is driven by converging advances in nanotechnology, biomaterials, controlled release mechanisms, wireless communication, artificial intelligence, and additive manufacturi...

Multiple Targets and Pathways Non-Monotonically Regulate Lung Squamous Cell Carcinoma Migration in Response to Phthalates.

Phthalates (PAEs) are widely used plasticizers that are increasingly linked to tumorigenesis. In this study, we integrated network toxicology and machine learning to elucidate the potential mechanisms of PAEs in lung squamous cell carcinoma (LUSC). By integrating multi-database target prediction, The Cancer Genome Atlas Program (TCGA) transcriptomic analysis, and algorithmic screening, four key ge...

Managing multidrug resistant Gram-negative bacteraemia in immunocompromised hosts.

PURPOSE OF REVIEW: Multidrug-resistant Gram-negative bloodstream infections (MDR-GNBSI) are increasingly frequent in immunocompromised hosts, particul...

Prostate Cancer Detection on Micro-Ultrasound Raw Data Using a Deep Learning Neural Network.

BACKGROUND: Micro-ultrasound (micro-US) is a clinically available novel high-resolution imaging technology for guiding prostate biopsies. However, cli...

Risk classification of oral potentially malignant disorders (OPMDs) using explainable machine learning through a community-based cross-sectional study in rural India.

BACKGROUND: Oral cancer is a major public health problem among the population of India. In rural setting whereby there is exposure to high-risk factor...

Beyond Weight Loss: Obesogenic Memory as Biological Hysteresis in Adipose Tissue Revealed by AI Semantic Mapping With an Exportable Core Corpus.

BACKGROUND: Obesity is commonly viewed as a reversible condition primarily driven by excess body weight. Increasing evidence, however, suggests that a...

Reexamining the Uncanny Valley in Palliative Care: Attunement in the Age of Artificial Intelligence.

Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.

OBJECTIVE: To provide a structured narrative review of current evidence and future directions for artificial intelligence (AI) applications in the ter...

Deep learning model for noninvasive prediction of Ki-67 expression and prognostic stratification in breast cancer: a multicenter retrospective study.

OBJECTIVE: Ki-67 correlates with prognosis for patients with breast cancer. However, the evaluation of Ki-67expression relies on pathological analysis...

Reconstruction algorithms and arm positioning effects on abdominal CT image quality and radiation dose: a phantom study.

OBJECTIVE: To evaluate the effects of arm positioning and reconstruction algorithms on radiation dose and image quality of abdominal CT. MATERIALS AND...

Identification of RPL4 as a biomarker associated with disulfidptosis and arachidonic acid metabolism in breast cancer.

OBJECTIVE: Disulfidptosis is a recently discovered mechanism of cell death caused by disulfide stress. Arachidonic acid metabolism (AAM) is one of the...

Autoencoder/RandomForest-TabPFN for Cross-Cancer Metabolomics: Prostate and Breast Cancer Diagnosis Using Paper Spray and Ion Mobility-Mass Spectrometry Techniques.

Accurate and rapid disease diagnosis, particularly in prostate cancer (PC) and breast cancer (BC), is critical for early intervention and improved pat...

Metabolite-centric identification of antimetabolite drug targets across cancer and neurodegenerative diseases.

Antimetabolites, primarily studied in cancer, are novel drugs targeting metabolic networks by mimicking and inhibiting disease-causing metabolites, en...

Artificial intelligence in cancer diagnostics: a primer for clinicians and scientists.

Artificial Intelligence (AI) is rapidly transforming cancer care by enabling healthcare teams to make more accurate diagnoses, predict responses to th...

Pancreatic cancer.

Pancreatic ductal adenocarcinoma remains one of the deadliest malignancies, characterized by late diagnosis, aggressive biology and limited therapeuti...

Prediction of SARS-CoV-2 exposure through T cell activation profiles.

In the ongoing effort to study the SARS-CoV-2 virus and COVID-19 disease, assessment of the T cell immune response has guided vaccine and therapeutic ...

An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.

Prognostic stratification in gastric cancer (GC) currently relies on the tumour-node-metastasis (TNM) staging system, which incompletely captures tumo...

Commercial AI for CT lung cancer screening: product capabilities, coverage of nodule management tasks and supporting evidence.

OBJECTIVES: To characterize the capabilities of CE-marked AI products for lung nodule analysis in lung cancer screening (LCS), quantify their coverage...

Machine learning-based prognostic signature integrating mitochondrial function and programmed cell death patterns in pancreatic adenocarcinoma.

The interplay between mitochondria and programmed cell deaths (PCD) is associated with tumor pathogenesis. However, the specific roles of genes relate...

Integrated multi-omics and single-cell analyses identify metabolic heterogeneity and therapeutic vulnerabilities in medullary thyroid cancer.

BACKGROUND: Medullary thyroid cancer (MTC) is a heterogeneous and aggressive malignancy with limited therapeutic options. Metabolic reprogramming, a h...