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S100A6 promotes liver metastasis by activating FGFR3 signaling in <i>BAP1</i>-deficient uveal melanoma

✇Oncogene - Issue - nature.com science feeds
By: Yonghao Li · Churun Zheng · Yanting Liang · Yue Liu · Fanfan Zhou · Xue Zhu · Jing Fang · Renhua Gai · Yanyan Zhuang · Ke Wang · Tao Yuan · Qiaojun He · Bo Yang · Xin Dong · Hong Zhu

Oncogene, Published online: 04 April 2026; doi:10.1038/s41388-026-03766-0

S100A6 promotes liver metastasis by activating FGFR3 signaling in BAP1-deficient uveal melanoma
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Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mengxian Lyu · Cheng Peng · Ziyi Chen · Mengyuan Zhang · Jieting Li Lu · Yonghui Wu
arXiv:2604.01538v1 Announce Type: cross Abstract: Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a significant amount of instruction-following ability when fine-tuned using a task-specific medical dataset, a critical challenge in adopting general-purpose LLMs for clinical applications. This study presents a model merging framework to efficiently adapt general-purpose LLMs to the medical domain by countering this forgetting issue. By merging a clinical foundation model (GatorTronLlama) with a general instruct model (Llama-3.1-8B-Instruct) via interpolation-based merge methods, we seek to derive a domain-adapted model with strong performance on clinical tasks while retaining instruction-following ability. Comprehensive evaluation across medical benchmarks and five clinical generation tasks (e.g., radiology and discharge summarization) shows that merged models can effectively mitigate catastrophic forgetting, preserve clinical domain expertise, and retain instruction-following ability. In addition, our model merging strategies demonstrate training efficiency, achieving performance on par with fully fine-tuned baselines under severely constrained supervision (e.g., 64-shot vs. 256-shot). Consequently, weight-space merging constitutes a highly scalable solution for adapting open-source LLMs to clinical applications, facilitating broader deployment in resource-constrained healthcare environments.
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Dual Antitumor Effects of Solanum incanum L. in Lung and Colonic Adenocarcinomas via TRAIL-Mediated Extrinsic Apoptotic Pathway: A Network Pharmacology and Multi-Omics Investigation

✇Omics In Lung
By: Chih-Ting Lin · Li-Ren Yeh · Jiun-Ting Wu · Chao-Ming Hung · Chang-Yin Lee

Curr Med Chem. 2026 Mar 17. doi: 10.2174/0109298673415187251212085809. Online ahead of print.

ABSTRACT

BACKGROUND: Solanum incanum L. (S. incanum) is a Traditional Chinese Medicine (TCM) known for its heat-clearing and detoxifying properties. This study evaluates the anti-cancer potential of S. incanum in lung and colorectal adenocarcinoma. Also, we aim to confirm the connection between the lung and the large intestine, as proposed in TCM theory.

METHODS: This study employed network pharmacology analysis, MTT and morphological analysis, Western blotting, and molecular docking to investigate the anti-cancer effects of S. incanum, particularly through its activation of the TRAIL-mediated extrinsic apoptotic pathway.

RESULTS: This study identified 15 active compounds in S. incanum, with glycosylated alkaloids such as α-solanine and solasonine highlighted as key bioactive compounds. KEGG and GO analyses revealed that S. incanum influences apoptosis-related pathways, particularly the extrinsic apoptotic pathway, regulating mediators such as CASP3, CASP8, and various TNF- related receptors. The anticancer potential was assessed through cellular studies using A549 and HT29 cell lines, with MTT cell viability assays showing that α-solanine induces apoptosis, with IC50 values between 20 and 30 μM. Western blot analysis demonstrated that S. incanum activates apoptosis via the TRAIL-mediated extrinsic apoptotic pathway, promoting CASP8, CASP3, and PARP1 cleavage in a dose-dependent manner, affecting DNA synthesis. Molecular docking further identified α-solanine and solasonine as compounds that bind to critical proteins in the TRAIL-related apoptotic pathway, confirming their roles in promoting apoptosis.

DISCUSSION: The shared activation of the TRAIL-mediated apoptotic pathway in both lung and colorectal adenocarcinoma models suggests a common molecular mechanism. These findings provide experimental evidence linking traditional ethnopharmacological concepts with apoptosis-related anti-cancer activity.

CONCLUSIONS: This study emphasizes that the main compound of S. incanum, α-solanine, supports the ethnopharmacological concept of "Exterior-interior Pairing of the Lung and Large Intestine", thereby demonstrating that both lung and colorectal adenocarcinomas share a common anticancer pathway through a TRAIL-related apoptotic mechanism.

PMID:41863159 | DOI:10.2174/0109298673415187251212085809

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Human-specific features of the cerebellum and ZP2-regulated synapse development

✇Cell
By: Suel-Kee Kim · Adriana Cherskov · Aastha Sindhwani · Sang-Hun Choi · Hyojin Kim · Ming-Li Li · Menglei Zhang · Xoel Mato-Blanco · Yuting Liu · Nicola Micali · David M. Young · Mark Estacion · Yueqi Zhang · José Manuel Ruiz-Jiménez · Anandita Nadkarni · Victor Luria · Suvimal Kumar Sindhu · Ipsita Chatterjee · Akemi Shibata · Dan Liang · Hyesun Cho · Saejeong Park · Ana Spajic · Rothem Kovner · Martina Glavan · Rachel J. Chen · Ryan D. Risgaard · Xinyun Li · Sirisha Pochareddy · Amir Karger · Anita Huttner · Yury M. Morozov · Etienne W. Daadi · Carlo Colantuoni · Kevin T. Gobeske · John J. Ely · Patrick R. Hof · Marcel M. Daadi · Chet C. Sherwood · Alvaro Duque · Shaojie Ma · Andre M.M. Sousa · Stephen G. Waxman · Pasko Rakic · Gabriel Santpere · Stephan J. Sanders · Nenad Sestan
Human-specific transcriptomic and regulatory features are present in the cerebellum, with ZP2 playing a key role in synapse regulation. ZP2 expression is induced by pontine mossy fibers, leading to decreased synaptic proteins and neuronal activity, which provides insights into the evolutionary development of the human cerebellum.
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NFATC2::NUTM2 Fusion Defines a Novel Primary Pulmonary Epithelial Tumor With a Distinctive Immunophenotype

✇Pulmonary nodule
By: Lanlan Feng · Qiao Liu · Xiaoyan Liu · Shumei Wang · Junting Li · Yu Han · Xiaorong Mu · Yuanyuan Wang · Junfeng Jiang · Chun Wu · Xiujuan Han · Shaojun Zhu · Fuqin Zhang · Jing Tian · Zhuo Wang · Wei Zhang · Li Gong

Am J Surg Pathol. 2026 Jun 1;50(6):695-704. doi: 10.1097/PAS.0000000000002533. Epub 2026 Mar 13.

ABSTRACT

With the application of molecular techniques in pathologic diagnosis, several novel primary pulmonary epithelial tumors have been continuously discovered and classified under the WHO classification of thoracic tumors. Recently, a pulmonary tumor with NFATC2 :: NUTM2B fusion was first documented, but the spectrum of NFATC2::NUTM2 fusion variants and their associated pathologic features remains incompletely characterized. Coincidentally, we also found and described 6 primary pulmonary tumors harboring recurrent NFATC2::NUTM2A/E fusions through integrated genomic analysis. These patients, including 4 females and 2 males, with a median age of 53 years, presented with incidentally detected peripheral lung nodules composed of monotonous epithelioid cells arranged in cords, nests, and trabeculae within a prominent desmoplastic stroma. All tumors exhibited a consistent immunophenotype: CK5/6+/GATA3+/calponin+/EMA+/DOG1 (perinuclear dot-like staining)/p63-. High-throughput chromosome conformation capture (Hi-C) analysis showed the structural variation of NFATC2::NUTM2E in all 6 cases, whereas RNA sequencing detected the fusion transcripts in 5 cases ( NFATC2::NUTM2A , n=2; NFATC2::NUTM2E , n=3). Ultrastructural examination of 1 case suggested epithelial differentiation. All patients remained disease-free after complete resection (median follow-up: 24 mo; range: 9 to 41 mo). These findings define a novel primary pulmonary tumor entity driven by NFATC2::NUTM2 fusions, and characterized by a distinctive immunophenotype, expanding the spectrum of NUTM2 -associated neoplasms. Our study underscores the utility of multiomics approaches for characterizing rare neoplasms and provides a diagnostic framework for this entity.

PMID:41821426 | DOI:10.1097/PAS.0000000000002533

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NFATC2::NUTM2 Fusion Defines a Novel Primary Pulmonary Epithelial Tumor With a Distinctive Immunophenotype

✇Omics In Lung
By: Lanlan Feng · Qiao Liu · Xiaoyan Liu · Shumei Wang · Junting Li · Yu Han · Xiaorong Mu · Yuanyuan Wang · Junfeng Jiang · Chun Wu · Xiujuan Han · Shaojun Zhu · Fuqin Zhang · Jing Tian · Zhuo Wang · Wei Zhang · Li Gong

Am J Surg Pathol. 2026 Mar 13. doi: 10.1097/PAS.0000000000002533. Online ahead of print.

ABSTRACT

With the application of molecular techniques in pathologic diagnosis, several novel primary pulmonary epithelial tumors have been continuously discovered and classified under the WHO classification of thoracic tumors. Recently, a pulmonary tumor with NFATC2::NUTM2B fusion was first documented, but the spectrum of NFATC2::NUTM2 fusion variants and their associated pathologic features remains incompletely characterized. Coincidentally, we also found and described 6 primary pulmonary tumors harboring recurrent NFATC2::NUTM2A/E fusions through integrated genomic analysis. These patients, including 4 females and 2 males, with a median age of 53 years, presented with incidentally detected peripheral lung nodules composed of monotonous epithelioid cells arranged in cords, nests, and trabeculae within a prominent desmoplastic stroma. All tumors exhibited a consistent immunophenotype: CK5/6+/GATA3+/calponin+/EMA+/DOG1 (perinuclear dot-like staining)/p63-. High-throughput chromosome conformation capture (Hi-C) analysis showed the structural variation of NFATC2::NUTM2E in all 6 cases, whereas RNA sequencing detected the fusion transcripts in 5 cases (NFATC2::NUTM2A, n=2; NFATC2::NUTM2E, n=3). Ultrastructural examination of 1 case suggested epithelial differentiation. All patients remained disease-free after complete resection (median follow-up: 24 mo; range: 9 to 41 mo). These findings define a novel primary pulmonary tumor entity driven by NFATC2::NUTM2 fusions, and characterized by a distinctive immunophenotype, expanding the spectrum of NUTM2-associated neoplasms. Our study underscores the utility of multiomics approaches for characterizing rare neoplasms and provides a diagnostic framework for this entity.

PMID:41821426 | DOI:10.1097/PAS.0000000000002533

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T2S-Bench & Structure-of-Thought: Benchmarking and Prompting Comprehensive Text-to-Structure Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qinsi Wang · Hancheng Ye · Jinhee Kim · Jinghan Ke · Yifei Wang · Martin Kuo · Zishan Shao · Dongting Li · Yueqian Lin · Ting Jiang · Chiyue Wei · Qi Qian · Wei Wen · Helen Li · Yiran Chen
arXiv:2603.03790v1 Announce Type: cross Abstract: Think about how human handles complex reading tasks: marking key points, inferring their relationships, and structuring information to guide understanding and responses. Likewise, can a large language model benefit from text structure to enhance text-processing performance? To explore it, in this work, we first introduce Structure of Thought (SoT), a prompting technique that explicitly guides models to construct intermediate text structures, consistently boosting performance across eight tasks and three model families. Building upon this insight, we present T2S-Bench, the first benchmark designed to evaluate and improve text-to-structure capabilities of models. T2S-Bench includes 1.8K samples across 6 scientific domains and 32 structural types, rigorously constructed to ensure accuracy, fairness, and quality. Evaluation on 45 mainstream models reveals substantial improvement potential: the average accuracy on the multi-hop reasoning task is only 52.1%, and even the most advanced model achieves 58.1% node accuracy in end-to-end extraction. Furthermore, on Qwen2.5-7B-Instruct, SoT alone yields an average +5.7% improvement across eight diverse text-processing tasks, and fine-tuning on T2S-Bench further increases this gain to +8.6%. These results highlight the value of explicit text structuring and the complementary contributions of SoT and T2S-Bench. Dataset and eval code have been released at https://t2s-bench.github.io/T2S-Bench-Page/.
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IDER: IDempotent Experience Replay for Reliable Continual Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhanwang Liu · Yuting Li · Haoyuan Gao · Yexin Li · Linghe Kong · Lichao Sun · Weiran Huang
arXiv:2603.00624v2 Announce Type: replace-cross Abstract: Catastrophic forgetting, the tendency of neural networks to forget previously learned knowledge when learning new tasks, has been a major challenge in continual learning (CL). To tackle this challenge, CL methods have been proposed and shown to reduce forgetting. Furthermore, CL models deployed in mission-critical settings can benefit from uncertainty awareness by calibrating their predictions to reliably assess their confidences. However, existing uncertainty-aware continual learning methods suffer from high computational overhead and incompatibility with mainstream replay methods. To address this, we propose idempotent experience replay (IDER), a novel approach based on the idempotent property where repeated function applications yield the same output. Specifically, we first adapt the training loss to make model idempotent on current data streams. In addition, we introduce an idempotence distillation loss. We feed the output of the current model back into the old checkpoint and then minimize the distance between this reprocessed output and the original output of the current model. This yields a simple and effective new baseline for building reliable continual learners, which can be seamlessly integrated with other CL approaches. Extensive experiments on different CL benchmarks demonstrate that IDER consistently improves prediction reliability while simultaneously boosting accuracy and reducing forgetting. Our results suggest the potential of idempotence as a promising principle for deploying efficient and trustworthy continual learning systems in real-world applications.Our code is available at https://github.com/YutingLi0606/Idempotent-Continual-Learning.
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Can Multimodal LLMs See Science Instruction? Benchmarking Pedagogical Reasoning in K-12 Classroom Videos

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yixuan Shen · Peng He · Honglu Liu · Yuyang Ji · Tingting Li · Tianlong Chen · Kaidi Xu · Feng Liu
arXiv:2602.18466v1 Announce Type: cross Abstract: K-12 science classrooms are rich sites of inquiry where students coordinate phenomena, evidence, and explanatory models through discourse; yet, the multimodal complexity of these interactions has made automated analysis elusive. Existing benchmarks for classroom discourse focus primarily on mathematics and rely solely on transcripts, overlooking the visual artifacts and model-based reasoning emphasized by the Next Generation Science Standards (NGSS). We address this gap with SciIBI, the first video benchmark for analyzing science classroom discourse, featuring 113 NGSS-aligned clips annotated with Core Instructional Practices (CIP) and sophistication levels. By evaluating eight state-of-the-art LLMs and Multimodal LLMs, we reveal fundamental limitations: current models struggle to distinguish pedagogically similar practices, suggesting that CIP coding requires instructional reasoning beyond surface pattern matching. Furthermore, adding video input yields inconsistent gains across architectures. Crucially, our evidence-based evaluation reveals that models often succeed through surface shortcuts rather than genuine pedagogical understanding. These findings establish science classroom discourse as a challenging frontier for multimodal AI and point toward human-AI collaboration, where models retrieve evidence to accelerate expert review rather than replace it.
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Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wuzhenghong Wen · Bowen Zhou · Jinwen Huang · Xianjie Wu · Yuwei Sun · Su Pan · Liang Li · Jianting Liu
arXiv:2602.19569v1 Announce Type: cross Abstract: Question Answering over Temporal Knowledge Graphs (TKGQA) has attracted growing interest for handling time-sensitive queries. However, existing methods still struggle with: 1) weak incorporation of temporal constraints in question representation, causing biased reasoning; 2) limited ability to perform explicit multi-hop reasoning; and 3) suboptimal fusion of language and graph representations. We propose a novel framework with temporal-aware question encoding, multi-hop graph reasoning, and multi-view heterogeneous information fusion. Specifically, our approach introduces: 1) a constraint-aware question representation that combines semantic cues from language models with temporal entity dynamics; 2) a temporal-aware graph neural network for explicit multi-hop reasoning via time-aware message passing; and 3) a multi-view attention mechanism for more effective fusion of question context and temporal graph knowledge. Experiments on multiple TKGQA benchmarks demonstrate consistent improvements over multiple baselines.
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