The tumor microenvironment (TME) is a complex and dynamic ecosystem that plays a pivotal role in cancer progression and therapy resistance. Its heterogeneity and constant remodeling present significant challenges for effective treatment. The emergence of nanomedicine integrates nanotechnology and medicine, aiming to overcome and ameliorate the limitations of conventional therapeutic agents in cancer treatment. Despite the broad biomedical applications of nanomaterials, the clinical translation of nanodrugs remains hindered by the complexity and heterogeneity of the TME as well as challenges related to the physicochemical properties of nanomaterials. This paper published in iMetaMed underscores key challenges and difficulties currently faced by nanomaterials in cancer treatment, including: issues related to nanomaterial biosafety and long-term toxicity assessment; uncertainties in vivo biotransformation and metabolic pathways; therapeutic efficacy variations caused by spatiotemporal heterogeneity of the TME; barriers from laboratory research to clinical translation; and insufficient selectivity in the precise modulation of TME components.

Biomedical research data visualization faces several challenges, including insufficient expertise and fragmented methodologies, which severely limit research efficiency and result quality. FigureYa is a standardized visualization framework composed of 317 modular R scripts, rather than a standalone software or desktop application. It covers key domains such as expression profiling, immune analysis, survival analysis, and single-cell data visualization. Based on the concept of “replace data and use,” FigureYa significantly lowers the technical threshold, allowing researchers to generate high-quality charts without requiring an extensive programming background. Compared to generic online R code snippets, FigureYa offers rigorously developed, thoroughly validated, and biologically contextualized visualization modules originally written by the author team. Each script includes version-matched environments, example datasets, and detailed annotations, providing clear advantages in automation, reproducibility, and scientific professionalism, thereby providing a standardized visualization solution for complex biomedical data. This innovative tool optimizes research time allocation, promotes interdisciplinary collaboration, accelerates scientific discovery and clinical translation, and provides robust data visualization support for biomedical research.

A KIER research team led by Dr. Yu-Jin Han and Dr. Sang-Hoon Park has developed a core technology to refine industrial graphite byproducts into high-purity anode materials for lithium-ion batteries, a breakthrough that could greatly lessen reliance on imported graphite.

Lower back pain is the most common musculoskeletal issue in the U.S. and a top cause of global disability. To tackle this, researchers have developed a groundbreaking AI-powered system that automates patient-specific lumbar spine modeling. By merging deep learning with biomechanical simulation, the new method slashes model prep time by nearly 98% – from more than 24 hours to just 30 minutes – while preserving clinical accuracy. This innovation enables faster, more consistent diagnoses and personalized treatment planning.