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Received today — 7 October 2026 ⏭

Multi-Omics and Computational Pharmacology Approach With Experimental Validation Reveals the Antiproliferative Activity of Sophoricoside Against Pancreatic Cancer

✇Omics in Hepatocellular
By: Peng Lin · Wei Cheng · Xin Qi · Jing Li
30 September 2026 at 18:00

Chem Biodivers. 2026 Oct;23(10):e71778. doi: 10.1002/cbdv.71778.

ABSTRACT

Pancreatic cancer has a dismal prognosis and limited therapeutic options, highlighting an urgent need for effective treatments. Sophoricoside (SOP), a natural isoflavone glycoside, has exhibited anticancer activities in multiple malignancies, including lung cancer, glioblastoma, and hepatocellular carcinoma. We combined cellular assays, network pharmacology, machine learning, and multi-omics to investigate SOP's effects. SOP-inhibited proliferation of MIA PaCa-2, SW1990, and PANC-1 cells dose-dependently. Network pharmacology revealed 85 overlapping targets enriched in MAPK, apoptosis, and PD-L1/PD-1 pathways. Machine learning and differential expression identified PTPN1 as the core target. PTPN1 was markedly upregulated in pancreatic adenocarcinoma, and its high expression correlated with poor survival and immune infiltration. Functional enrichment linked PTPN1 to TGF-β, VEGF, and metabolic reprogramming. Molecular docking suggested a possible binding mode between SOP and PTPN1, involving four predicted hydrogen bonds. SOP reduced PTPN1 mRNA, and PTPN1 knockdown phenocopied SOP's antiproliferative effect with no additivity upon combination. Collectively, this first report demonstrates that SOP restrains pancreatic cancer cell proliferation, with PTPN1 identified as a key functionally required downstream mediator based on integrative computational and functional evidence. This work offers an integrated strategy for mechanistic exploration and highlights PTPN1 as a promising therapeutic biomarker and target for pancreatic cancer.

PMID:42814531 | PMC:PMC13626263 | DOI:10.1002/cbdv.71778

Multi-Omics and Computational Pharmacology Approach With Experimental Validation Reveals the Antiproliferative Activity of Sophoricoside Against Pancreatic Cancer

✇(Multiomics OR Omics) AND (Pancreatic)
By: Peng Lin · Wei Cheng · Xin Qi · Jing Li
30 September 2026 at 18:00

Chem Biodivers. 2026 Oct;23(10):e71778. doi: 10.1002/cbdv.71778.

ABSTRACT

Pancreatic cancer has a dismal prognosis and limited therapeutic options, highlighting an urgent need for effective treatments. Sophoricoside (SOP), a natural isoflavone glycoside, has exhibited anticancer activities in multiple malignancies, including lung cancer, glioblastoma, and hepatocellular carcinoma. We combined cellular assays, network pharmacology, machine learning, and multi-omics to investigate SOP's effects. SOP-inhibited proliferation of MIA PaCa-2, SW1990, and PANC-1 cells dose-dependently. Network pharmacology revealed 85 overlapping targets enriched in MAPK, apoptosis, and PD-L1/PD-1 pathways. Machine learning and differential expression identified PTPN1 as the core target. PTPN1 was markedly upregulated in pancreatic adenocarcinoma, and its high expression correlated with poor survival and immune infiltration. Functional enrichment linked PTPN1 to TGF-β, VEGF, and metabolic reprogramming. Molecular docking suggested a possible binding mode between SOP and PTPN1, involving four predicted hydrogen bonds. SOP reduced PTPN1 mRNA, and PTPN1 knockdown phenocopied SOP's antiproliferative effect with no additivity upon combination. Collectively, this first report demonstrates that SOP restrains pancreatic cancer cell proliferation, with PTPN1 identified as a key functionally required downstream mediator based on integrative computational and functional evidence. This work offers an integrated strategy for mechanistic exploration and highlights PTPN1 as a promising therapeutic biomarker and target for pancreatic cancer.

PMID:42814531 | PMC:PMC13626263 | DOI:10.1002/cbdv.71778

Author Correction: Intermittent hypobaric pressure induces selective senescent cell death and alleviates age-related osteoporosis

✇Nature Biomedical Engineering
By: Bowen Meng · Yan Qu · Benyi Yang · Chaoran Fu · Yifan He · Jing Li · Rentao Wan · Xin Li · Zhulin Xue · Zeyuan Cao · Meng Hao · Xiao Zhang · Zhe An · Fen Chen · Ruibao Ren · Xueli Mao · Yang Cao · Songtao Shi
28 September 2026 at 08:00

Nature Biomedical Engineering, Published online: 28 September 2026; doi:10.1038/s41551-026-01815-3

Author Correction: Intermittent hypobaric pressure induces selective senescent cell death and alleviates age-related osteoporosis
Received — 15 September 2026 ⏭

Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue

✇cs.AI, q-bio.NC updates on arXiv.org
By: Youyuan Zhang · Siyuan Li · Fangming Liu · Jing Li
14 September 2026 at 12:00
arXiv:2609.12373v1 Announce Type: new Abstract: Persona drift remains a central challenge for personalized language models, as user profiles evolve over long interactions rather than remain permanently fixed. Models must therefore revise persistent persona states when preferences genuinely change, while avoiding updates driven by transient, ambiguous, or unresolved observations. We propose CORE, which separates turn-local evidence from persistent persona-state revision and selectively updates grounded user preferences through uncertainty-aware belief revision. We also introduce PERSIST, a held-out post-anchor benchmark for persona-state robustness under sequential interaction stress, covering ambiguity, conflict, and controlled social influence. Across ALOE, PersonaChat, and PERSIST, CORE improves personalized alignment and robustness, with complementary gains in normalized closed-slot state fidelity. Human evaluation and mechanistic controls further support explicit update control beyond stronger generation or persistent memory alone.

UFO: Chain-of-Evaluation for Omni-Condition Alignment in Multi-Modal Image Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Danning Zhang · Yijing Lin · Shuhan Zhuang · Mengqi Huang · Shaojin Wu · Shancheng Fang · Zhendong Mao
14 September 2026 at 12:00
arXiv:2609.12397v1 Announce Type: cross Abstract: Multi-modal image generation, particularly subject-driven customization, has garnered growing attention in recent years. Despite the rapid advancement of generative models, their evaluation remains largely lagging. Existing methods, whether embedding-based or Multi-modal Large Language Model (MLLM)-based, evaluate alignment with each modal condition in isolation, which contradicts the simultaneous condition alignment objective of multi-modal image generation, leading to poor consistency with human judgments. To address this challenge, we propose UFO, the first unified framework for omni-condition alignment simultaneous evaluation. Specifically, UFO introduces a novel Atomized Chain-of-Evaluation paradigm, \emph{i.e.}, it first decomposes omni-condition alignment into a sequential chain of fine-grained, disentangled Atomic Evaluation Units (AEUs), categorizes them into distinct modality-relevance classes, and then employs general or dedicated functional calls for accurate verification of different AEU types. Experimental results demonstrate that UFO achieves the highest correlation with human evaluation preferences, delivering an average improvement of 15.25\%. Furthermore, we present UFO-Bench, a dedicated benchmark designed to holistically evaluate the performance of existing customization models under the diverse mutual interactions of textual and visual conditions.

ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xianming Li · Zongxi Li · Tsz-fung Andrew Lee · Jing Li · Haoran Xie · Qing Li
14 September 2026 at 12:00
arXiv:2604.19254v2 Announce Type: replace-cross Abstract: Popular low-rank parameter-efficient fine-tuning (PEFT) methods represent adaptation as separate updates to selected backbone weights, without maintaining an explicit task-specific state that is updated and reused across depth. These updates also require the backbone at inference and therefore cannot operate as standalone predictors. We propose ShadowPEFT, which consolidates trainable adaptation into a modular shadow component centered on a compact shadow model and lightweight Transformer layer-specific coupling modules. A persistent shadow state refines the frozen backbone representations and is updated from them in an interactive manner. Because the shadow model is trained as a complete predictor, it can be detached for shadow-only inference without executing the base model and can be initialized from a pretrained model. Experiments on text and image generation and understanding benchmarks show that ShadowPEFT matches or outperforms LoRA and DoRA under comparable trainable-parameter budgets. Additional analyses on shadow pretraining, cross-dataset transfer, parameter scaling, inference latency, and system-level evaluation suggest that centralized layer-space adaptation is a competitive and flexible alternative to conventional low-rank PEFT.
Received — 13 September 2026 ⏭

A clinically-oriented foundation model for intraoperative pathology

✇Nature Medicine
By: Zihan Zhao · Fengtao Zhou · Ronggang Li · Bing Chu · Xinke Zhang · Xueyi Zheng · Ke Zheng · Xiaobo Wen · Jiabo Ma · Yihui Wang · Jiewei Chen · Chengyou Zheng · Jiangyu Zhang · Yongqin Wen · Jiajia Meng · Ziqi Zeng · Xiaoqing Li · Jing Li · Dan Xie · Yaping Ye · Yu Wang · Hao Chen · Muyan Cai
10 September 2026 at 08:00

Nature Medicine, Published online: 10 September 2026; doi:10.1038/s41591-026-04703-0

CRISP, a vision-based pathology foundation model developed exclusively from frozen section slides, supports treatment decision-making throughout the surgical workflow with superior performance to current foundation models and extensive validation, including in a prospective cohort.
Received — 10 September 2026 ⏭

From Diet to Disease: The Role of the Gut Microbiome and Microbial Metabolites in Horses

✇Omics in Gastric
By: Yuhong Wu · Yuhui Ma · Yiyi Zan · Xu Bai · Jing Li · Hai Li
27 August 2026 at 18:00

Vet Sci. 2026 Aug 18;13(8):823. doi: 10.3390/vetsci13080823.

ABSTRACT

The equine gastrointestinal microbiome plays an essential role in digestion, energy metabolism, immune regulation, and maintenance of intestinal homeostasis. Increasing evidence suggests that microbial-derived metabolites provide a functional link between diet, the microbiome, and host physiology. This narrative review summarizes current knowledge on how dietary factors, including structural carbohydrates, non-structural carbohydrates, protein, and lipids, influence microbial function and metabolite production in horses. Relevant publications available up to June 2026 were identified through searches of PubMed, Web of Science, Scopus, and Google Scholar and were narratively synthesized according to dietary factors, microbial metabolites, associated diseases, and nutritional interventions. Emphasis is placed on major microbial metabolites, including short-chain fatty acids, endotoxins, bile acid derivatives, tryptophan metabolites, and nitrogenous fermentation products, and their potential roles in health and disease. Current evidence supports a central role for the nutrition-microbiota-metabolite axis in gastrointestinal disorders such as colic, colitis, equine gastric ulcer syndrome, and carbohydrate-associated laminitis, whereas its involvement in equine metabolic syndrome and the gut-lung axis remains less well defined. The review also discusses microbiome-targeted nutritional interventions and emerging multi-omics approaches that are improving our understanding of microbial function. Collectively, these findings highlight the potential of microbiome-informed nutritional strategies to support equine health, welfare, disease prevention, and athletic performance.

PMID:42655843 | PMC:PMC13517798 | DOI:10.3390/vetsci13080823

Received — 27 May 2026 ⏭

Towards Multi-Turn Dialog Systems for Industrial Asset Operations and Maintenance

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chengrui Li · Rujing Li · Yitong Bai · Rui Li
26 May 2026 at 12:00
arXiv:2605.24953v1 Announce Type: new Abstract: Industrial asset operations and maintenance question answering is inherently multi-turn, iterative, and highly dependent on external tool invocation. However, the conventional plan-execute single-agent architecture exhibits clear limitations in maintaining cross-turn context, and reusing intermediate results. In this paper, we present a multi-turn dialog system designed for industrial scenarios based on a supervisor-specialist multi-agent architecture. To alleviate tool invocation bottlenecks, the system incorporates structured artifact reuse, dynamic replanning, and parallel tool execution. Evaluation results show that our system achieves better response quality compared with the baseline, with planning effectiveness increasing by 54.5% and task completion improving by 37.8%. System profiling further shows that cross-turn artifact reuse effectively reduces redundant tool invocation, decreasing the tool-time share from 47.3% to 26.3% and making turns 2-5 approximately 4.2x faster than the first turn.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yangneng Chen · Jing Li
26 May 2026 at 12:00
arXiv:2605.25036v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) extend large language models with visual understanding, but remain vulnerable to hallucination, where outputs are fluent yet inconsistent with images. Recent studies link this issue to language bias-the tendency of LVLMs to over-rely on text while neglecting visual inputs. Yet most analyses remain empirical without uncovering its underlying cause. In this paper, we provide a systematic study of language bias and identify its root in modality misalignment during training. Our analysis shows that both Visual Instruction Tuning (VIT) and Direct Preference Optimization (DPO) often prioritize textual improvements, which may cause LVLMs to overly lean toward language modeling rather than balanced multimodal understanding. To address this, we propose two simple yet effective methods: Language Bias Regularization (LBR) which mitigates language bias through regularization during instruction tuning, and Language Bias Penalty (LBP), which penalizes language bias in the DPO training process. Extensive experiments across diverse models and benchmarks demonstrate the effectiveness of our approach. LBR consistently improves performance on over ten general benchmarks, while LBP significantly reduces hallucination and improves trustworthiness. Together, these methods not only mitigate language bias but also advance the overall alignment of LVLMs, all without introducing any additional data or auxiliary models. Our code is publicly available at https://github.com/lab-klc/LVLM-Language-Bias.

Dynamic Dual-Granularity Skill Bank for Agentic RL

✇cs.AI, q-bio.NC updates on arXiv.org
By: Songjun Tu · Chengdong Xu · Qichao Zhang · Yaocheng Zhang · Xiangyuan Lan · Linjing Li · Dong Li · Dongbin Zhao
26 May 2026 at 12:00
arXiv:2603.28716v2 Announce Type: replace Abstract: Agentic RL can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often lack principled mechanisms for maintaining an evolving skill memory. We propose D2Skill, a dynamic dual-granularity skill bank for agentic RL that organizes reusable experience into task skills for high-level guidance and step skills for fine-grained decision support and error correction. D2Skill jointly trains the policy and skill bank through paired baseline and skill-injected rollouts under the same policy, using their performance gap to derive hindsight utility signals for both skill updating and policy optimization. Built entirely from training-time experience, the skill bank is continuously expanded through reflection and maintained with utility-aware retrieval and pruning. Experiments on ALFWorld, WebShop, and Search-Augmented QA tasks show that D2Skill substantially improves performance over skill-free baselines across models of different scales. Further ablations and analyses show that both dual-granularity skill modeling and dynamic skill maintenance are critical to these gains, while the learned skills exhibit higher utility, transfer across evaluation settings, and introduce only modest training overhead.

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyu Huang · Boyu Liu · Linlin Yang · Yanjing Li · Yuguang Yang · Xuhui Liu · Canyu Chen · Zhongqian Fu · Baochang Zhang
26 May 2026 at 12:00
arXiv:2605.10989v3 Announce Type: replace-cross Abstract: The training of Binary Neural Networks (BNNs) is fundamentally based on gradient approximation for non-differentiable binarization operations (e.g., sign function). However, prevailing methods including the Straight-Through Estimator (STE) and its improved variants, rely on hand-crafted designs that suffer from gradient mismatch problem and information loss induced by fixed-range gradient clipping. To address this, we propose SURrogate GradiEnt Adaptation (SURGE), a novel learnable gradient compensation framework with theoretical grounding. SURGE mitigates gradient mismatch through auxiliary backpropagation. Specifically, we design a Dual-Path Gradient Compensator (DPGC) that constructs a parallel full-precision auxiliary branch for each binarized layer, decoupling gradient flow via output decomposition during backpropagation. DPGC enables bias-reduced gradient estimation by leveraging the full-precision branch to estimate components beyond STE's first-order approximation. To further enhance training stability, we introduce an Adaptive Gradient Scaler (AGS) based on an optimal scale factor to dynamically balance inter-branch gradient contributions via norm-based scaling. Experiments on image classification, object detection, and language understanding tasks demonstrate that SURGE performs best over state-of-the-art methods.
Received — 13 April 2026 ⏭

Single-cell spatiotemporal dissection of the human maternal–fetal interface

✇Nature - Issue - nature.com science feeds
By: Cheng Wang · Yan Zhou · Yuejun Wang · Tuhin Kumar Guha · Zhida Luo · Anxhela Mustafaraj · Tara I. McIntyre · Marisa E. Schwab · Brittany R. Davidson · Gabriella C. Reeder · Ronald J. Wong · Sarah K. England · Juan M. Gonzalez · Robert Blelloch · Alexis J. Combes · Linda C. Giudice · Adrian Erlebacher · Tippi C. MacKenzie · David K. Stevenson · Gary M. Shaw · Michael P. Snyder · Xiaofei Sun · Virginia D. Winn · Susan J. Fisher · Jingjing Li
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10316-x

A single-cell multiomic atlas of the human maternal–fetal interface across pregnancy reveals cell types, states and spatial niches, developmental tissue architectures and transcriptional programmes, and identifies cell types with roles in pre-eclampsia, spontaneous preterm birth and miscarriage.
Received — 4 April 2026 ⏭

Efficient amyloid-β degradation in Alzheimer’s disease using SPYTACs

✇Cell
By: Fei Teng · Jing Liu · Tongtong Cui · Xiangtian Tan · Kailun Liu · Zongren Hou · Li Zhou · Yuanzhi Xie · Rongqi Li · Da Li · Bojin Li · Dongmei Wang · Qi Zhou · Baoyang Hu · Wei Li
4 March 2026 at 08:00
SPYTAC is a synthetic peptide-programmed targeted protein degradation platform harnessing LRP1 to drive lysosomal degradation of extracellular amyloid-β in the brain and periphery. In 5×FAD mice, SPYTAC treatment efficiently degrades amyloid-β, preserves neurons, and improves cognition with reduced neuroinflammation and microhemorrhage when compared with antibody therapy.
Received — 2 April 2026 ⏭

Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haozhen Gong · Xiaozhong Ji · Yuansen Liu · Wenbin Wu · Xiaoxiao Yan · Jingjing Liu · Kai Wu · Jiazhen Pan · Bailiang Jian · Jiangning Zhang · Xiaobin Hu · Hongwei Bran Li
1 April 2026 at 12:00
arXiv:2512.00818v2 Announce Type: replace Abstract: MLLMs MLLMs are beginning to appear in clinical workflows, but their ability to perform complex medical reasoning remains unclear. We present Med-CMR, a fine-grained Medical Complex Multimodal Reasoning benchmark. Med-CMR distinguishes from existing counterparts by three core features: 1) Systematic capability decomposition, splitting medical multimodal reasoning into fine-grained visual understanding and multi-step reasoning to enable targeted evaluation; 2) Challenging task design, with visual understanding across three key dimensions (small-object detection, fine-detail discrimination, spatial understanding) and reasoning covering four clinically relevant scenarios (temporal prediction, causal reasoning, long-tail generalization, multi-source integration); 3) Broad, high-quality data coverage, comprising 20,653 Visual Question Answering (VQA) pairs spanning 11 organ systems and 12 imaging modalities, validated via a rigorous two-stage (human expert + model-assisted) review to ensure clinical authenticity. We evaluate 18 state-of-the-art MLLMs with Med-CMR, revealing GPT-5 as the top-performing commercial model: 57.81 accuracy on multiple-choice questions (MCQs) and a 48.70 open-ended score, outperforming Gemini 2.5 Pro (49.87 MCQ accuracy, 45.98 open-ended score) and leading open-source model Qwen3-VL-235B-A22B (49.34 MCQ accuracy, 42.62 open-ended score). However, specialized medical MLLMs do not reliably outperform strong general models, and long-tail generalization emerges as the dominant failure mode. Med-CMR thus provides a stress test for visual-reasoning integration and rare-case robustness in medical MLLMs, and a rigorous yardstick for future clinical systems.
Received — 26 March 2026 ⏭

WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis

✇npj Digital Medicine
By: Yuqi Wu · Guangya Wan · Jingjing Li · Shengming Zhao · Lingfeng Ma · Tianyi Ye · Mike Zhang · Ion Pop · Yanbo Zhang · Jie Chen
25 March 2026 at 08:00

npj Digital Medicine, Published online: 25 March 2026; doi:10.1038/s41746-026-02559-9

WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis

Comprehensive multi omics profiling and Mendelian randomization assessment of lipid metabolites in lung cancer prognosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhiwei Xu · Jingjing Li
23 March 2026 at 18:00

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Comprehensive multi omics profiling and Mendelian randomization assessment of lipid metabolites in lung cancer prognosis

✇Omics In Lung
By: Zhiwei Xu · Jingjing Li
23 March 2026 at 18:00

Discov Oncol. 2026 Mar 23. doi: 10.1007/s12672-026-04893-6. Online ahead of print.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide. This study aimed to develop prognostic prediction models for lung squamous cell carcinoma (LUSC) through multi-omics integration using Mendelian randomization analysis.This study addresses a critical gap in lung cancer research through two complementary approaches in major lung cancer subtypes: (1) hypothesis-generating multi-omics analysis in LUSC to identify prognostic biomarkers and characterize the metabolic-immune landscape. This integrated framework provides both predictive tools for personalized medicine and mechanistic insights into metabolic causality.

METHODS: Multi-omics analysis was performed using TCGA data, including RNA-seq, DNA methylation, and whole-exome sequencing. Machine learning models incorporating 15 algorithms were developed and externally validated in two independent GEO cohorts. Mendelian randomization analysis assessed causal relationships between 32 lipid metabolites and SCLC risk. RT-qPCR experiments validated key prognostic genes in lung squamous cell carcinoma (LUSC) cell lines.

RESULTS: The optimal machine learning model (StepCox [forward] + Random Survival Forest) demonstrated superior performance with C-index of 0.73 in internal testing and 0.71 and 0.68 in external validation cohorts. High CD8 + T cell and M1 macrophage infiltration was associated with favorable prognosis. Most lipid metabolites showed no significant causal associations with SCLC risk after multiple testing correction, though two phosphatidylcholine metabolites demonstrated potential protective effects. RT-qPCR validation confirmed significant upregulation of all four key genes in LUSC cell lines.

CONCLUSIONS: This study successfully developed robust machine learning-based prognostic models for LUSC with clinical utility for risk stratification and provided evidence that lipid alterations in lung cancer are likely downstream consequences rather than causal drivers of tumorigenesis.

PMID:41870745 | DOI:10.1007/s12672-026-04893-6

Received — 17 March 2026 ⏭

Thinking in Streaming Video

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zikang Liu · Longteng Guo · Handong Li · Ru Zhen · Xingjian He · Ruyi Ji · Xiaoming Ren · Yanhao Zhang · Haonan Lu · Jing Liu
16 March 2026 at 12:00
arXiv:2603.12938v1 Announce Type: cross Abstract: Real-time understanding of continuous video streams is essential for interactive assistants and multimodal agents operating in dynamic environments. However, most existing video reasoning approaches follow a batch paradigm that defers reasoning until the full video context is observed, resulting in high latency and growing computational cost that are incompatible with streaming scenarios. In this paper, we introduce ThinkStream, a framework for streaming video reasoning based on a Watch--Think--Speak paradigm that enables models to incrementally update their understanding as new video observations arrive. At each step, the model performs a short reasoning update and decides whether sufficient evidence has accumulated to produce a response. To support long-horizon streaming, we propose Reasoning-Compressed Streaming Memory (RCSM), which treats intermediate reasoning traces as compact semantic memory that replaces outdated visual tokens while preserving essential context. We further train the model using a Streaming Reinforcement Learning with Verifiable Rewards scheme that aligns incremental reasoning and response timing with the requirements of streaming interaction. Experiments on multiple streaming video benchmarks show that ThinkStream significantly outperforms existing online video models while maintaining low latency and memory usage. Code, models and data will be released at https://github.com/johncaged/ThinkStream
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