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Spatial multi-omics unveils the monoclonal origin, neuroendocrine plasticity, and microenvironment niches in combined small-cell lung cancer

Cell Rep Med. 2026 Apr 10:102741. doi: 10.1016/j.xcrm.2026.102741. Online ahead of print.

ABSTRACT

Combined small-cell lung cancer (cSCLC) is an aggressive subtype of SCLC with mixed histologic components. Despite heterogeneity and poorer prognosis than de novo SCLC, cSCLC is managed as SCLC because molecular insight into biology, lineage plasticity, and tumor microenvironment (TME) is limited. We perform spatial whole-exome sequencing, spatial transcriptomics, and single-nucleus RNA sequencing across 19 treatment-naive cSCLC tumors. Different histologic components share a monoclonal origin, whereas divergence associates with distinct mutation and copy-number alteration patterns. Our results define spatially exclusive or interspersed tumor domains with distinct TME and immune landscapes; fibroblast-rich boundaries enriched for an aggressive fibroblast subtype may shape TME and treatment responses. We identify lineage plasticity, including adenocarcinoma-to-SCLC transdifferentiation and SCLC-subtype coexistence, and develop cSCLC Detector, a sensitive mutation-based assay improving cSCLC detection in tissue and liquid biopsies. These findings illuminate cSCLC evolution and heterogeneity, underscoring the need for tailored diagnostic and therapeutic strategies for this aggressive subtype.

PMID:41966692 | DOI:10.1016/j.xcrm.2026.102741

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Spatial multi-omics unveils the monoclonal origin, neuroendocrine plasticity, and microenvironment niches in combined small-cell lung cancer

Cell Rep Med. 2026 Apr 10:102741. doi: 10.1016/j.xcrm.2026.102741. Online ahead of print.

ABSTRACT

Combined small-cell lung cancer (cSCLC) is an aggressive subtype of SCLC with mixed histologic components. Despite heterogeneity and poorer prognosis than de novo SCLC, cSCLC is managed as SCLC because molecular insight into biology, lineage plasticity, and tumor microenvironment (TME) is limited. We perform spatial whole-exome sequencing, spatial transcriptomics, and single-nucleus RNA sequencing across 19 treatment-naive cSCLC tumors. Different histologic components share a monoclonal origin, whereas divergence associates with distinct mutation and copy-number alteration patterns. Our results define spatially exclusive or interspersed tumor domains with distinct TME and immune landscapes; fibroblast-rich boundaries enriched for an aggressive fibroblast subtype may shape TME and treatment responses. We identify lineage plasticity, including adenocarcinoma-to-SCLC transdifferentiation and SCLC-subtype coexistence, and develop cSCLC Detector, a sensitive mutation-based assay improving cSCLC detection in tissue and liquid biopsies. These findings illuminate cSCLC evolution and heterogeneity, underscoring the need for tailored diagnostic and therapeutic strategies for this aggressive subtype.

PMID:41966692 | DOI:10.1016/j.xcrm.2026.102741

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Integrated analysis of network pharmacology and multi-omics reveals the mechanisms of Zuogui Jiangtang Qinggan formula ameliorates MASLD via fatty acid metabolic reprogramming

Phytomedicine. 2026 Mar 30;155:158128. doi: 10.1016/j.phymed.2026.158128. Online ahead of print.

ABSTRACT

BACKGROUND: The global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, and its pathogenesis is complex, creating an urgent need to discover novel and effective therapeutic strategies. The Zuogui Jiangtang Qinggan formula (ZGJTQGF), an approved in-hospital preparation, has demonstrated significant clinical efficacy in treating diabetes over several decades. However, the mechanisms underlying its potential therapeutic effects on MASLD remain unclear PURPOSE: This study systematically investigates the therapeutic effects and molecular mechanisms of ZGJTQGF on MASLD through the integration of network pharmacology and multi-omics strategies.

METHODS: The model of MASLD was successfully induced in db/db mice by a high-fat diet (HFD), which displayed characteristic dyslipidaemia. Serum biomarkers, histology, and hepatic multi-omics analyses were employed to assess metabolic status, steatosis, targets, and pathways. Ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), molecular docking analysis and in vitro verification were applied to explore the active ingredients of ZGJTQGF.

RESULTS: ZGJTQGF significantly reduced dyslipidemia in HFD-fed mice, inhibited pro-inflammatory cytokines, and restored glucose metabolic balance by lowering levels of glucose, insulin, OGTT, and HOMA-IR. Histopathology showed reduced lipid deposition and hepatocyte damage. Comprehensive multi-omics analysis suggested that regulating the AMPK/PGC-1α/PPARα and FXR-BSEP signaling pathways could be potential targets for ZGJTQGF in reprogramming glucose and lipid metabolism in MASLD treatment. Blood component analysis identified 52 ZGJTQGF-derived compounds. In molecular docking experiments, Wogonin, Naringenin, Quercetin, Tanshinone IIA and Berberine showed high-affinity binding to core targets in AMPK, PPARα, PGC-1α, FXR and FAS. Mechanistically, ZGJTQGF activated AMPK/PPARα /PGC-1α and FXR-BSEP signaling pathway, promotes fatty acid β oxidation and enhances energy consumption in AML-2 and 3T3-L1 cells, downregulates SREBP-1-dependent adipogenesis (reduces ACC1 and FAS expression), alleviates MASLD driven reprogramming of glucose and lipid metabolism, and regulates lipid metabolism and fatty acid synthesis.

CONCLUSIONS: ZGJTQGF activates the AMPK/PPARα /PGC-1α pathway and inhibits abnormal lipid accumulation in diabetic fatty liver by promoting fatty acid β-oxidation, energy consumption, and bile acid metabolism. These findings provide new insights into the mechanism of ZGJTQGF in the treatment of diabetic fatty liver disease.

PMID:41962267 | DOI:10.1016/j.phymed.2026.158128

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Integrated analysis of network pharmacology and multi-omics reveals the mechanisms of Zuogui Jiangtang Qinggan formula ameliorates MASLD via fatty acid metabolic reprogramming

Phytomedicine. 2026 Mar 30;155:158128. doi: 10.1016/j.phymed.2026.158128. Online ahead of print.

ABSTRACT

BACKGROUND: The global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, and its pathogenesis is complex, creating an urgent need to discover novel and effective therapeutic strategies. The Zuogui Jiangtang Qinggan formula (ZGJTQGF), an approved in-hospital preparation, has demonstrated significant clinical efficacy in treating diabetes over several decades. However, the mechanisms underlying its potential therapeutic effects on MASLD remain unclear PURPOSE: This study systematically investigates the therapeutic effects and molecular mechanisms of ZGJTQGF on MASLD through the integration of network pharmacology and multi-omics strategies.

METHODS: The model of MASLD was successfully induced in db/db mice by a high-fat diet (HFD), which displayed characteristic dyslipidaemia. Serum biomarkers, histology, and hepatic multi-omics analyses were employed to assess metabolic status, steatosis, targets, and pathways. Ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), molecular docking analysis and in vitro verification were applied to explore the active ingredients of ZGJTQGF.

RESULTS: ZGJTQGF significantly reduced dyslipidemia in HFD-fed mice, inhibited pro-inflammatory cytokines, and restored glucose metabolic balance by lowering levels of glucose, insulin, OGTT, and HOMA-IR. Histopathology showed reduced lipid deposition and hepatocyte damage. Comprehensive multi-omics analysis suggested that regulating the AMPK/PGC-1α/PPARα and FXR-BSEP signaling pathways could be potential targets for ZGJTQGF in reprogramming glucose and lipid metabolism in MASLD treatment. Blood component analysis identified 52 ZGJTQGF-derived compounds. In molecular docking experiments, Wogonin, Naringenin, Quercetin, Tanshinone IIA and Berberine showed high-affinity binding to core targets in AMPK, PPARα, PGC-1α, FXR and FAS. Mechanistically, ZGJTQGF activated AMPK/PPARα /PGC-1α and FXR-BSEP signaling pathway, promotes fatty acid β oxidation and enhances energy consumption in AML-2 and 3T3-L1 cells, downregulates SREBP-1-dependent adipogenesis (reduces ACC1 and FAS expression), alleviates MASLD driven reprogramming of glucose and lipid metabolism, and regulates lipid metabolism and fatty acid synthesis.

CONCLUSIONS: ZGJTQGF activates the AMPK/PPARα /PGC-1α pathway and inhibits abnormal lipid accumulation in diabetic fatty liver by promoting fatty acid β-oxidation, energy consumption, and bile acid metabolism. These findings provide new insights into the mechanism of ZGJTQGF in the treatment of diabetic fatty liver disease.

PMID:41962267 | DOI:10.1016/j.phymed.2026.158128

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SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents

arXiv:2603.29139v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled agentic systems that translate natural language intent into executable scientific visualization (SciVis) tasks. Despite rapid progress, the community lacks a principled and reproducible benchmark for evaluating these emerging SciVis agents in realistic, multi-step analysis settings. We present SciVisAgentBench, a comprehensive and extensible benchmark for evaluating scientific data analysis and visualization agents. Our benchmark is grounded in a structured taxonomy spanning four dimensions: application domain, data type, complexity level, and visualization operation. It currently comprises 108 expert-crafted cases covering diverse SciVis scenarios. To enable reliable assessment, we introduce a multimodal outcome-centric evaluation pipeline that combines LLM-based judging with deterministic evaluators, including image-based metrics, code checkers, rule-based verifiers, and case-specific evaluators. We also conduct a validity study with 12 SciVis experts to examine the agreement between human and LLM judges. Using this framework, we evaluate representative SciVis agents and general-purpose coding agents to establish initial baselines and reveal capability gaps. SciVisAgentBench is designed as a living benchmark to support systematic comparison, diagnose failure modes, and drive progress in agentic SciVis. The benchmark is available at https://scivisagentbench.github.io/.
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Hydroxy-induced cobalt oxides for syngas to light olefins

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10204-4

A set of hydroxy promoters physically mixed with cobalt oxides for syngas to light olefins conversion using Fischer–Tropsch synthesis is shown to boost catalyst performance as well as simplifying the process and increasing sustainability.
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Non-enzymatic function of QSOX2 directly regulates the JUNB-ITGB4 axis and enhanced resistance to osimertinib in EGFR-mutation lung adenocarcinoma

Cell Death Discovery, Published online: 01 April 2026; doi:10.1038/s41420-026-02969-4

Non-enzymatic function of QSOX2 directly regulates the JUNB-ITGB4 axis and enhanced resistance to osimertinib in EGFR-mutation lung adenocarcinoma
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Disentangling Reasoning in Large Audio-Language Models for Ambiguous Emotion Prediction

arXiv:2603.08230v1 Announce Type: cross Abstract: Speech emotion recognition plays an important role in various applications. However, most existing approaches predict a single emotion label, oversimplifying the inherently ambiguous nature of human emotional expression. Recent large audio-language models show promise in generating richer outputs, but their reasoning ability for ambiguous emotional understanding remains limited. In this work, we reformulate ambiguous emotion recognition as a distributional reasoning problem and present the first systematic study of ambiguity-aware reasoning in LALMs. Our framework comprises two complementary components: an ambiguity-aware objective that aligns predictions with human perceptual distributions, and a structured ambiguity-aware chain-of-thought supervision that guides reasoning over emotional cues. Experiments on IEMOCAP and CREMA-D demonstrate consistent improvements across SFT, DPO, and GRPO training strategies.
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Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

arXiv:2602.10604v2 Announce Type: replace-cross Abstract: We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most when building agents: sharp reasoning and fast, reliable execution. Step 3.5 Flash pairs a 196B-parameter foundation with 11B active parameters for efficient inference. It is optimized with interleaved 3:1 sliding-window/full attention and Multi-Token Prediction (MTP-3) to reduce the latency and cost of multi-round agentic interactions. To reach frontier-level intelligence, we design a scalable reinforcement learning framework that combines verifiable signals with preference feedback, while remaining stable under large-scale off-policy training, enabling consistent self-improvement across mathematics, code, and tool use. Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and 51.0% on Terminal-Bench 2.0, comparable to frontier models such as GPT-5.2 xHigh and Gemini 3.0 Pro. By redefining the efficiency frontier, Step 3.5 Flash provides a high-density foundation for deploying sophisticated agents in real-world industrial environments.
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