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Received today — 7 October 2026 ⏭

SPTLC2-driven sphingolipid reprogramming of neutrophils impairs anti-tumour immunity and drives liver cancer progression

✇Oncogene - Issue - nature.com science feeds
By: Zhe Yang · Yun Wang · Jianhao Li · Zishuo Wang · Jianan Chen · Ding Zhang · Guorui Zhao · Liwen Liu · Zhibo Liu · Ning Wang · Ranran Sun
28 September 2026 at 08:00

Oncogene, Published online: 28 September 2026; doi:10.1038/s41388-026-03996-2

SPTLC2-driven sphingolipid reprogramming of neutrophils impairs anti-tumour immunity and drives liver cancer progression
Received — 10 September 2026 ⏭

The redox architecture of gestational diabetes mellitus: from cellular stress engine to epigenetic and mitochondrial rewiring

✇(Multiomics OR Omics) AND (Pancreatic)
By: Haijuan Zhang · Shaoxiong Zhang · Yuanhao Liu · Jinjiang Shi · Aijun Yang · Ruirui Jin · Yuxia Jin · Xuya Wang · Yitao Fan · Qian Liu · Yanhua Zhang · Cheng Wang · Jianjun Mao · Zhiyuan Zheng · Ling Lv · Muzhou Teng
9 September 2026 at 18:00

Free Radic Biol Med. 2026 Sep 9;256:441-460. doi: 10.1016/j.freeradbiomed.2026.09.006. Online ahead of print.

ABSTRACT

Gestational diabetes mellitus (GDM) is a common pregnancy complication with a rising global prevalence, posing serious short-term and long-term health threats to both mothers and offspring. This review repositions GDM as a systemic disorder in which oxidative stress acts as a proposed mechanistic hub, linking upstream risk factors to downstream pathophysiology. We first examine how "upstream" factors-including genetic susceptibility, pre-conception status, and environmental exposures-converge to promote a state of pathological redox imbalance. We then examine key mechanistic pathways through which oxidative stress is thought to contribute to systemic insulin resistance and pancreatic β-cell failure, highlighting novel pathways involving intercellular communication via tunneling nanotubes and exosomes. Furthermore, we explore the downstream cascade, where oxidative stress may program maternal accelerated biological aging and multi-organ offspring disease trajectories through nuclear epigenetic programming and mitochondrial dysfunction programming, leaving what has been termed a persistent "metabolic memory". Consequently, this review evaluates emerging strategies that target oxidative stress for early prediction and precision intervention. Early prediction models based on direct redox biomarkers and multi-omics signatures hold potential to shift diagnosis from late-gestation oral glucose tolerance test (OGTT) to first-trimester risk stratification. Current supporting evidence draws from human epidemiological associations, ex vivo placental analyses, and experimental models. However, direct causal and interventional validation in pregnant women remains limited. Integrating targeted redox risk stratification and precision interventions into a life-course clinical framework may help interrupt the intergenerational transmission of metabolic disease initiated by GDM.

PMID:42716407 | DOI:10.1016/j.freeradbiomed.2026.09.006

Received — 27 May 2026 ⏭

High-fidelity identification of guest species in porous materials

✇Nature - Issue - nature.com science feeds
By: Qilong Feng · Liang Wang · Yuanhao Li · Xiaoqiu Xu · Yuan Yao · Yue Liu · Yonghe Li · Tulai Sun · Changlin Zheng · Lina Li · Hui Jin · Chongzhi Zhu · Jia Zhao · Peng Guo · Xiaonian Li · Yu Han · Yihan Zhu
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10527-2

A reconstruction method based on Gaussian-apodized single-sideband electron ptychography removes artefacts to enable the high-fidelity identification of guest species in porous materials.
Received — 8 April 2026 ⏭

Embedding Enhancement via Fine-Tuned Language Models for Learner-Item Cognitive Modeling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuanhao Liu · Zihan Zhou · Kaiying Wu · Shuo Liu · Yiyang Huang · Jiajun Guo · Aimin Zhou · Hong Qian
7 April 2026 at 12:00
arXiv:2604.04088v1 Announce Type: cross Abstract: Learner-item cognitive modeling plays a central role in the web-based online intelligent education system by enabling cognitive diagnosis (CD) across diverse online educational scenarios. Although ID embedding remains the mainstream approach in cognitive modeling due to its effectiveness and flexibility, recent advances in language models (LMs) have introduced new possibilities for incorporating rich semantic representations to enhance CD performance. This highlights the need for a comprehensive analysis of how LMs enhance embeddings through semantic integration across mainstream CD tasks. This paper identifies two key challenges in fully leveraging LMs in existing work: Misalignment between the training objectives of LMs and CD models creates a distribution gap in feature spaces; A unified framework is essential for integrating textual embeddings across varied CD tasks while preserving the strengths of existing cognitive modeling paradigms to ensure the robustness of embedding enhancement. To address these challenges, this paper introduces EduEmbed, a unified embedding enhancement framework that leverages fine-tuned LMs to enrich learner-item cognitive modeling across diverse CD tasks. EduEmbed operates in two stages. In the first stage, we fine-tune LMs based on role-specific representations and an interaction diagnoser to bridge the semantic gap of CD models. In the second stage, we employ a textual adapter to extract task-relevant semantics and integrate them with existing modeling paradigms to improve generalization. We evaluate the proposed framework on four CD tasks and computerized adaptive testing (CAT) task, achieving robust performance. Further analysis reveals the impact of semantic information across diverse tasks, offering key insights for future research on the application of LMs in CD for online intelligent education systems.

KLong: Training LLM Agent for Extremely Long-horizon Tasks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yue Liu · Yingwei Ma · Yibo Miao · Yanhao Li · Yuchong Xie · Xinlong Yang · Zhiyuan Hu · Flood Sung · Jiaheng Zhang · Bryan Hooi
7 April 2026 at 12:00
arXiv:2602.17547v2 Announce Type: replace Abstract: This paper introduces KLong, an open-source LLM agent trained to solve extremely long-horizon tasks. The principle is to first cold-start the model via trajectory-splitting SFT, then scale it via progressive RL training. Specifically, we first activate basic agentic abilities of a base model with a comprehensive SFT recipe. Then, we introduce Research-Factory, an automated pipeline that generates high-quality training data by collecting research papers and constructing evaluation rubrics. Using this pipeline, we build thousands of long-horizon trajectories distilled from Claude 4.5 Sonnet (Thinking). To train with these extremely long trajectories, we propose a new trajectory-splitting SFT, which preserves early context, progressively truncates later context, and maintains overlap between sub-trajectories. In addition, to further improve long-horizon task-solving capability, we propose a novel progressive RL, which schedules training into multiple stages with progressively extended timeouts. Experiments demonstrate the superiority and generalization of KLong, as shown in Figure 1. Notably, our proposed KLong (106B) surpasses Kimi K2 Thinking (1T) by 11.28% on PaperBench, and the performance improvement generalizes to other coding benchmarks like SWE-bench Verified and MLE-bench.

FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tommy Sha (Kindred) · Zhan Cheng (Kindred) · Haotian Zhai (Kindred) · Xuwei Ding (Kindred) · Junnan Li (Kindred) · Haixiang Tang (Kindred) · Zaoting Sun (Kindred) · Yanchuan Tang (Kindred) · Yongzhe (Kindred) · Yi · Yuan Gao · Anhao Li
7 April 2026 at 12:00
arXiv:2511.08887v4 Announce Type: replace-cross Abstract: Stroke is an acute cerebrovascular disease, and timely diagnosis significantly improves patient survival. However, existing automated diagnosis methods suffer from fairness issues across demographic groups, potentially exacerbating healthcare disparities. In this work we propose FAST-CAD, a theoretically grounded framework that combines domain-adversarial training (DAT) with group distributionally robust optimization (Group-DRO) for fair and accurate non-contact stroke diagnosis. Our approach is built on domain adaptation and minimax fairness theory and provides convergence guarantees and fairness bounds. We curate a multimodal dataset covering 12 demographic subgroups defined by age, gender, and posture. FAST-CAD employs self-supervised encoders with adversarial domain discrimination to learn demographic-invariant representations, while Group-DRO optimizes worst-group risk to ensure robust performance across all subgroups. Extensive experiments show that our method achieves superior diagnostic performance while maintaining fairness across demographic groups, and our theoretical analysis supports the effectiveness of the unified DAT + Group-DRO framework. This work provides both practical advances and theoretical insights for fair medical AI systems.
Received — 2 April 2026 ⏭

Tracking vs. Deciding: The Dual-Capability Bottleneck in Searchless Chess Transformers

✇cs.AI, q-bio.NC updates on arXiv.org
By: Quanhao Li · Wei Jiang
1 April 2026 at 12:00
arXiv:2603.29761v1 Announce Type: new Abstract: A human-like chess engine should mimic the style, errors, and consistency of a strong human player rather than maximize playing strength. We show that training from move sequences alone forces a model to learn two capabilities: state tracking, which reconstructs the board from move history, and decision quality, which selects good moves from that reconstructed state. These impose contradictory data requirements: low-rated games provide the diversity needed for tracking, while high-rated games provide the quality signal for decision learning. Removing low-rated data degrades performance. We formalize this tension as a dual-capability bottleneck, P

Doxorubicin promotes the production of inflammatory cytokines in tumor-associated macrophages through activating lactate dehydrogenase A

✇Cell Death Discovery nature.com science feeds
By: Bin Liu · Wei Yang · Shuo Feng · Xingning Jiang · Jingfan Lu · Fei Wang · Yaowen Hu · Yanhao Liu · Haifeng Ma · Ting Sun
31 March 2026 at 08:00

Cell Death Discovery, Published online: 31 March 2026; doi:10.1038/s41420-026-03014-0

Doxorubicin promotes the production of inflammatory cytokines in tumor-associated macrophages through activating lactate dehydrogenase A
Received — 26 March 2026 ⏭

Spatial Omics in Gastrointestinal Oncology: Recent Advances, Therapeutic Insights, and Clinical Translation

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Lin Zhong · Qian Li · Ting Xiong · Shengzheng Lin · Kaining Wang · Guanying Li · Ye Yan · Jianhao Liu · Chuansong Xue
23 March 2026 at 18:00

J Cancer. 2026 Jan 30;17(3):515-523. doi: 10.7150/jca.127381. eCollection 2026.

ABSTRACT

Gastrointestinal (GI) cancers remain a leading cause of cancer-related morbidity and mortality worldwide, largely due to their molecular heterogeneity, complex tumor microenvironment (TME), and variable treatment responses. In recent years, the emergence of spatially resolved omics technologies-encompassing spatial transcriptomics, proteomics, metabolomics, and epigenomics-has revolutionized the ability to interrogate tumor architecture with unprecedented resolution. These methods enable precise mapping of cellular and molecular interactions within intact tissue contexts, thereby uncovering spatially defined niches that influence tumor progression, immune evasion, and therapeutic resistance. In GI malignancies such as colorectal, gastric, and esophageal cancers, spatial omics have provided critical insights into cancer-stromal-immune crosstalk, identified predictive biomarkers for immunotherapy and targeted agents, and guided the development of novel therapeutic strategies. This review synthesizes the latest advances in spatial omics applied to GI oncology over the past five years, with an emphasis on their integration into early diagnosis, treatment stratification, and real-time monitoring of therapeutic efficacy. We also discuss current challenges, including standardization, data integration, and clinical validation, as well as future directions for incorporating spatial profiling into routine oncology practice. By bridging the gap between bench discoveries and bedside applications, spatial omics hold transformative potential for achieving truly personalized treatment in gastrointestinal cancers.

PMID:41869445 | PMC:PMC13003551 | DOI:10.7150/jca.127381

Received — 5 March 2026 ⏭

Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalism

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zheyu Chen · Zhuohuan Li · Chuanhao Li
5 March 2026 at 13:00
arXiv:2603.03784v1 Announce Type: new Abstract: World models are essential for planning and evaluation in agentic systems, yet existing approaches lie at two extremes: hand-engineered simulators that offer consistency and reproducibility but are costly to adapt, and implicit neural models that are flexible but difficult to constrain, verify, and debug over long horizons. We seek a principled middle ground that combines the reliability of explicit simulators with the flexibility of learned models, allowing world models to be adapted during online execution. By targeting a broad class of environments whose dynamics are governed by the ordering, timing, and causality of discrete events, such as queueing and service operations, embodied task planning, and message-mediated multi-agent coordination, we advocate explicit, executable discrete-event world models synthesized directly from natural-language specifications. Our approach adopts the DEVS formalism and introduces a staged LLM-based generation pipeline that separates structural inference of component interactions from component-level event and timing logic. To evaluate generated models without a unique ground truth, simulators emit structured event traces that are validated against specification-derived temporal and semantic constraints, enabling reproducible verification and localized diagnostics. Together, these contributions produce world models that are consistent over long-horizon rollouts, verifiable from observable behavior, and efficient to synthesize on demand during online execution.
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