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Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution

Stereo-seq V2 facilitates single-cell-resolution spatial RNA mapping in FFPE samples through random primer capture, uncovering ncRNAs, host-pathogen transcriptome profiling, and spatial immune repertoires in situ.

PaperArena: An Evaluation Benchmark for Tool-Augmented Agentic Reasoning on Scientific Literature

arXiv:2510.10909v2 Announce Type: replace Abstract: Understanding and reasoning on the web-scale scientific literature is a crucial touchstone for large language model (LLM) based agents designed to support complex knowledge-intensive tasks. However, existing works are mainly restricted to tool-free tasks within isolated papers, largely due to the lack of a benchmark for cross-paper reasoning and multi-tool orchestration in real research scenarios. In this work, we propose PaperArena, an evaluation benchmark for agents to address real-world research questions that typically require integrating information across multiple papers with the assistance of external tools. Given a research question, agents should integrate diverse formats across multiple papers through reasoning and interacting with appropriate tools, thereby producing a well-grounded answer. To support standardized evaluation, we provide a modular and extensible platform for agent execution, offering tools such as multimodal parsing, context retrieval, and programmatic computation. Experimental results reveal that even the most advanced LLM powering a well-established agent system achieves merely 38.78% average accuracy. On the hard subset, accuracy drops to only 18.47%, highlighting great potential for improvement. We also present several empirical findings, including that all agents tested exhibit inefficient tool usage, often invoking more tools than necessary to solve a task. We invite the community to adopt PaperArena to develop and evaluate more capable agents for scientific discovery. Our code and data are available https://github.com/Melmaphother/PaperArena.

Annotation Guidelines-Based Knowledge Augmentation: Towards Enhancing Large Language Models for Educational Text Classification

arXiv:2406.00954v2 Announce Type: replace-cross Abstract: Various machine learning approaches have gained significant popularity for the automated classification of educational text to identify indicators of learning engagement -- i.e. learning engagement classification (LEC). LEC can offer comprehensive insights into human learning processes, attracting significant interest from diverse research communities, including Natural Language Processing (NLP), Learning Analytics, and Educational Data Mining. Recently, Large Language Models (LLMs), such as ChatGPT, have demonstrated remarkable performance in various NLP tasks. However, their comprehensive evaluation and improvement approaches in LEC tasks have not been thoroughly investigated. In this study, we propose the Annotation Guidelines-based Knowledge Augmentation (AGKA) approach to improve LLMs. AGKA employs GPT 4.0 to retrieve label definition knowledge from annotation guidelines, and then applies the random under-sampler to select a few typical examples. Subsequently, we conduct a systematic evaluation benchmark of LEC, which includes six LEC datasets covering behavior classification (question and urgency level), emotion classification (binary and epistemic emotion), and cognition classification (opinion and cognitive presence). The study results demonstrate that AGKA can enhance non-fine-tuned LLMs, particularly GPT 4.0 and Llama 3 70B. GPT 4.0 with AGKA few-shot outperforms full-shot fine-tuned models such as BERT and RoBERTa on simple binary classification datasets. However, GPT 4.0 lags in multi-class tasks that require a deep understanding of complex semantic information. Notably, Llama 3 70B with AGKA is a promising combination based on open-source LLM, because its performance is on par with closed-source GPT 4.0 with AGKA. In addition, LLMs struggle to distinguish between labels with similar names in multi-class classification.

Liquid Biopsy in CRC Management: Early Detection, Minimal Residual Disease, and Therapy Optimization-Clinical Evidence and Challenges

4 September 2025 at 18:00

Diagn Cytopathol. 2025 Nov;53(11):580-591. doi: 10.1002/dc.70009. Epub 2025 Sep 4.

ABSTRACT

Colorectal cancer (CRC) is a major global health burden, ranking among the leading causes of cancer-related deaths. Despite improvements in screening and treatment, challenges such as late-stage diagnosis, high recurrence rates, and therapy resistance continue to impede optimal outcomes. Liquid biopsy, a minimally invasive technique that analyzes tumor-derived components in bodily fluids-including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs)-is emerging as a powerful tool to transform CRC management across the disease continuum. This review provides a comprehensive overview of liquid biopsy's current and emerging applications in CRC. We examine its role in early detection, where sensitive ctDNA-based assays and epigenetic biomarkers have demonstrated the ability to identify CRC at asymptomatic or early stages, potentially improving screening uptake and compliance. Furthermore, we explore how liquid biopsy enables dynamic monitoring of treatment response and clonal evolution, facilitating the timely identification of resistance mutations and supporting personalized therapy adjustments. Innovations in multi-omics integration, artificial intelligence, and ultra-sensitive sequencing technologies are also discussed as pivotal advancements that enhance the clinical utility of liquid biopsy. Despite significant progress, the widespread adoption of liquid biopsy faces several hurdles, including assay standardization, sensitivity for low-shedding tumors, regulatory approval, and cost-effectiveness. Continued research, validation in large prospective trials, and harmonization of testing protocols are essential to overcome these challenges. Ultimately, liquid biopsy holds the potential to become a cornerstone of precision oncology in CRC, enabling earlier intervention, more tailored treatment strategies, and improved patient outcomes.

PMID:40905096 | DOI:10.1002/dc.70009

Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution

Cell. 2025 Aug 22:S0092-8674(25)00922-5. doi: 10.1016/j.cell.2025.08.008. Online ahead of print.

ABSTRACT

Performing total RNA profiling on formalin-fixed, paraffin-embedded (FFPE) samples, the predominant sample conservation method in clinical practice, remains challenging for current spatial transcriptomics techniques. Here, we introduce Stereo-seq V2, which employs random primers to capture and sequence RNAs in situ on FFPE sections and provides single-cell resolution. The random-priming-based strategy offers unbiased transcript capturing and uniform gene body coverage, which increase the sensitivity to marker genes, the efficiency of non-polyadenylation (poly(A)) RNA profiling, and immune repertoire coverage. We demonstrated the robust performance of Stereo-seq V2 on clinical FFPE samples using triple-negative breast cancer (TNBC) sections and identified tumor-specific alternative splicing events. In a Mycobacterium tuberculosis (Mtb)-infected mouse model, we monitored gene expression dynamics of host and pathogen transcriptomes simultaneously by utilizing Stereo-seq V2. We also assembled immune repertoires and identified Mtb-specific BCR clones, which could also be observed in human tuberculous lung samples. These results highlight Stereo-seq V2's potential in biomedical research and personalized medicine.

PMID:40882628 | DOI:10.1016/j.cell.2025.08.008

Spatiotemporal control of necroptotic cell death and plasma membrane recruitment using engineered MLKL domains

Cell Death Discovery, Published online: 29 November 2022; doi:10.1038/s41420-022-01258-0

Spatiotemporal control of necroptotic cell death and plasma membrane recruitment using engineered MLKL domains
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