Core Insights - The article discusses the anticipated hot research areas for the National Natural Science Foundation of China (NSFC) funding in 2026, emphasizing the importance of understanding these trends for project design [4][5]. Group 1: Core Mechanisms - Key focus areas include immune regulation (macrophage polarization, neutrophil function, T-cell exhaustion), mitochondrial function and homeostasis, novel cell death mechanisms (ferroptosis, pyroptosis), metabolic reprogramming (glycolysis, lactylation), and post-translational modifications of proteins (ubiquitination, SUMOylation) [4]. Group 2: Frontier Interdisciplinary Areas - Emerging fields highlighted are epitranscriptomics (RNA modifications like m6A, m7G), intercellular communication (exosomes), host-microbiome interactions (gut microbiota), and stem cells with regenerative medicine [5]. Group 3: Key Technologies - Important technological advancements include single-cell and spatial multi-omics, organoids and disease models, and AI/machine learning-driven target discovery and data analysis [5]. Group 4: Research Case Studies - The article presents several research cases that exemplify how researchers are engaging with these hot topics, including: - Neutrophils and their role in thrombosis, published in the European Heart Journal [12]. - Immune evasion mechanisms in tumors, published in Cell [13]. - Gut microbiota's role in Crohn's disease, published in Gut [14]. - Glycolysis and its implications in cancer, published in Signal Transduction and Targeted Therapy [14]. - Development of organoids for bone repair, published in Advanced Materials [14].

Core Viewpoint - The article discusses a systematic evaluation of computational tools for detecting multiple types of RNA modifications using Nanopore Direct RNA Sequencing (DRS), highlighting the performance differences and limitations of existing algorithms [1][2]. Group 1: Research Findings - A high-quality, single-base resolution benchmark dataset was constructed as a "gold standard" to evaluate 86 RNA modification detection algorithms based on DRS technology across four dimensions: accuracy, biological relevance, cross-sample generalization ability, and computational efficiency [2]. - The model retraining strategy significantly improved detection performance, with combined training on in vitro transcribed (IVT) RNA and real biological samples enhancing prediction accuracy and generalization, particularly for Ψ, m5C, and A-to-I modifications [4]. - m6A detection tools performed well overall, with models like Dorado and SingleMod excelling in qualitative and quantitative analysis, while most non-m6A modification detection tools struggled with quantitative accuracy and cross-sample generalization [5]. - Biological relevance is a crucial criterion, as ideal detection tools should not only have high accuracy but also align with known biological patterns; some tools showed discrepancies in predicted modification site distributions [5]. - Current tools face challenges in reliably distinguishing different modifications occurring on the same base, leading to "fuzzy predictions," which is a key area for future algorithm optimization [4]. - The retrained models can adapt to the iterative advancements in DRS technology, effectively addressing the challenges posed by changes in signal characteristics with the upgrade from RNA002 to RNA004 [6]. Group 2: Resource Development - To promote field development, the research team launched NaRMBench, an online resource platform that consolidates 12 key performance indicators of each tool into an interactive radar chart, aiding users in selecting and comparing analysis tools [8].