First immune-transcriptomic map of lung cancer plus tuberculosis.

CD4/CD8 ratio and NK cells up; antigen presentation pathways enriched.

Six-gene signature validated; four-gene model AUC 0.94 for LC-PTB.

MTB remodels tumor microenvironment via metabolic-immune crosstalk.

This review systematically examines the integration of machine learning (ML) and artificial intelligence (AI) in nanomedicine for cancer drug delivery. It demonstrates how ML algorithms—including support vector machines, neural networks, and deep learning models—are revolutionizing nanoparticle design, drug release prediction, and personalized therapy planning. The article outlines the complete ML workflow from data acquisition to model interpretation, compares key algorithms, and presents real-world case studies spanning multidrug carrier optimization and cancer diagnostics. While highlighting substantial preclinical advances, the authors identify critical barriers to clinical translation such as data heterogeneity, model opacity, and regulatory challenges. The review concludes with a forward-looking roadmap emphasizing data standardization, explainable AI, and clinical validation to bridge the gap between computational innovation and patient-ready nanomedicine.

 

Results from a VHIO-led study show that MYC targeting by Omomyc induces tumor DNA damage in patient-derived preclinical models of BRCA1/2-mutated triple-negative breast cancer.

Omomyc-induced DNA damage, combined with PARP inhibitors (PARPi) that block single-stranded DNA repair mechanisms, creates a synergistic effect against cancer cells that could help to overcome PARPi resistance in this patient population.

An AI-assisted analysis of tiny particles circulating in blood or urine—scientifically known as exosomes—could one day enable rapid and simple identification of cancer biomarkers. This promising insight stems from an extensive narrative review of the literature, offering a comprehensive and interpretative synthesis of published research on the topic. The findings have been published in the international journal Clinica Chimica Acta.

mRNA, widely known from the COVID-19 vaccine, is not actually a “therapeutic agent,” but a technology that delivers the blueprint for functional proteins into the body so that induces therapeutic effects. Recently, its application has expanded to cancer and genetic disease treatments, but mRNA therapeutics have caused serious side effects such as pulmonary embolism, stroke, thrombosis, and autoimmune diseases because proteins are excessively produced all at once immediately after administration. Although technology to control the endogenous protein factory has been continuously needed, there had been no suitable solution.

A new generation of targeted treatments and gentler chemotherapy options for older adults with a new diagnosis of acute myeloid leukemia (AML) is driving better survival and cure rates.

A new study published in the journal Scientific Reports shows that Claudin-4, a protein that increases in ovarian cancer, may be the culprit behind the cancer’s resistance, helping tumors both survive and hide from the body’s immune system.

Cells stop dividing when telomeres become too short to protect chromosomes, a process known as replicative senescence. But what drives it, and why cells senesce far earlier under high-oxygen conditions than under low-oxygen conditions, was not fully understood. The study shows that replicative senescence is enforced solely by the ATM kinase, and that high oxygen generates a hyperactive form of ATM that forces cells to arrest earlier. In low oxygen states, ATM’s less active form tolerates shorter telomeres. Because most tumors experience low oxygen levels, their reduced ATM response could allow cancer cells to tolerate very short telomeres, raising the possibility that reactivating ATM could stop tumor growth.