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Received — 13 September 2026 ⏭

Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis

✇Journal of Medical Internet Research
By: Yujia Lv · Lihui Zhou · Sihan Jiao · Jiaying Lin · Boran Sun · Haixia Hao · Chenxiao Jia · Yuan Wang · Li Bu · Wenli Lu
12 September 2026 at 04:45
Background: Obstructive sleep apnea (OSA) is highly prevalent but remains substantially underdiagnosed. Polysomnography (PSG) is the reference standard, but its cost and limited availability constrain large-scale case identification. AI-based screening tools may support risk stratification and referral prioritization, but their diagnostic accuracy across apnea-hypopnea index (AHI) thresholds remains uncertain. Objective: This review aimed to systematically evaluate the diagnostic accuracy of AI-based OSA screening tools at AHI thresholds of ≥5, ≥15, and ≥30 events/hour, with emphasis on models using non-PSG–derived inputs. Methods: PubMed, Embase, Scopus, and Web of Science were searched for studies published from January 1, 2016, to May 3, 2026. Eligible studies included adults evaluated for suspected OSA or recruited from population-based cohorts, assessed AI-based models intended or interpretable for OSA screening, risk prediction, or screening-oriented severity classification, used PSG as the reference standard, and reported sufficient data to construct or reconstruct 2×2 contingency tables. Diagnostic accuracy was synthesized separately by AHI threshold and input source using bivariate random-effects models, with 95% CIs and prediction intervals (PIs). Risk of bias and certainty of evidence were assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation), respectively. Results: A total of 60 studies were included, of which 47 contributed data to the meta-analysis. At AHI thresholds of ≥5, ≥15, and ≥30 events/hour, pooled sensitivities were 0.94 (95% CI 0.92‐0.96; 95% PI 0.71‐0.99), 0.87 (95% CI 0.84‐0.89; 95% PI 0.66‐0.96), and 0.83 (95% CI 0.79‐0.87; 95% PI 0.61‐0.94), respectively; the corresponding specificities were 0.77 (95% CI 0.69‐0.84; 95% PI 0.30‐0.96), 0.81 (95% CI 0.75‐0.85; 95% PI 0.39‐0.96), and 0.91 (95% CI 0.87‐0.94; 95% PI 0.55‐0.99), respectively. The corresponding areas under the summary receiver operating characteristic curves were 0.943, 0.907, and 0.920. For non-PSG–derived tools, sensitivities were 0.92, 0.85, and 0.81, and specificities were 0.70, 0.74, and 0.85 at the 3 thresholds, respectively. For PSG-derived models, sensitivities were 0.96, 0.90, and 0.85, and specificities were 0.82, 0.88, and 0.96, respectively. Exploratory subgroup analyses suggested performance variation across selected study and model characteristics, including region, algorithmic framework, data source, and validation method. Conclusions: AI-based tools showed generally favorable screening performance for OSA across clinically relevant AHI thresholds, although wide PIs suggest variable performance across future comparable populations and settings. By synthesizing diagnostic accuracy across 3 AHI thresholds and distinguishing non-PSG–derived from PSG-derived models, this review extends previous broad or modality-specific reviews and offers a clinically interpretable, pathway-specific basis for linking model performance to intended use. The findings may clarify potential roles for non-PSG–derived tools in front-end screening and referral prioritization and for PSG-derived models in reduced-channel assessment and sleep-laboratory workflow support. Given substantial heterogeneity, limited external validation, and low or very low certainty of evidence, prospective validation is needed before routine implementation.
Received — 10 September 2026 ⏭

Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaying Li · Haifeng Wen · Changsheng You · Yuanwei Liu · Hong Xing
10 September 2026 at 12:00
arXiv:2609.09409v1 Announce Type: cross Abstract: Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems, but its performance is often degraded by scattering-induced coherent propagation. Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high computation overhead and accumulated errors, and usually do not provide any guarantee on reliability. In this paper, we propose \emph{MUSIC-Net}, an end-to-end near-field positioning deep learning (DL) framework informed by two-stage MUltiple SIgnal Classification (MUSIC) in mixed line-of-sight (LoS) and non-LoS (NLoS) multi-path scenarios, which embeds the two-stage MUSIC objects into training to isolate the LoS-related signal subspace and to identify a surrogate distance. The proposed framework directly recovers multi-user positions without the need for involved NLoS parameter estimation or path/source association. Furthermore, we introduce split conformal prediction (SCP) to move beyond point-estimation-based positioning towards statistically guaranteed (confidence) set estimation for all users. Numerical results show that the proposed MUSIC-Net achieves lower mean positioning error (MPER) than existing benchmarks and yields tighter SCP-calibrated prediction regions, demonstrating both accurate LoS localization and efficient uncertainty quantification (UQ) in coherent multi-path environments.

LogiScope-VQA: Benchmarking Vision-Language Models for Logistics Hazard Identification in Industrial Scenarios

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hanjing Zhou · Mingze Yin · Ying Lian · Jun Ma · Chang-Yu Hsieh · Yanbing Zhou
10 September 2026 at 12:00
arXiv:2609.09790v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) large-scale deployment in industrial warehouse settings specifically necessitates that models exhibit human-expert-level hazard-oriented perception, understanding, and reasoning capabilities. However, the scarcity of real industrial data, tightly coupled to commercial terms, significantly hampers further advancement. To bridge this gap, we curate LogiScope-VQA to investigate the practical applicability of mainstream LMMs in real-world logistics operations. LogiScope-VQA comprises 2,476 images and 2,918 videos primarily sourced from real-world logistics parks, along with 10,274 VQAs meticulously curated and validated by human annotators. Grounded in 18 core objects and 20 risk types, we devise 39 subtasks aligned with three principal themes: industrial element perception, warehouse knowledge understanding, and potential risk reasoning. Furthermore, we incorporate dynamic thinking-budget configurations and dual-dimensional risk bias analyses to elucidate the properties of LMMs. Extensive experiments unveil that even powerful proprietary models, including GPT-5.5, Gemini-3.1-Pro, and Claude-Opus-4.7, exhibit a significant gap relative to human performance. The unique challenge of jointly integrating perception, understanding, and reasoning for hazard identification poses substantial headroom for further improvement on LogiScope-VQA. We additionally reveal the pervasive security bias issue that impedes LLMs' practical deployment in real-world settings. The industrial dataset is publicly available under the CC BY-NC-SA 4.0 license.

BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junfeng Xia · Wenhao Ye · Junxiang Zhang · Jiayu Zuo · Mo Wang · Quanying Liu
10 September 2026 at 12:00
arXiv:2609.10518v1 Announce Type: cross Abstract: fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study whether measured learning relations can organize both stages without modifying the backbone. During pretraining, a lightweight Brain-DiT proxy estimates difficulty and directed facilitation across ten fMRI domains, yielding a priority-guided cumulative domain curriculum combined with high-to-low-noise timestep scheduling and joint consolidation. During adaptation, controlled first- and higher-order transfer across fifteen tasks constructs a directed taskonomy, from which budgeted integer programming (BIP) selects directly supervised source tasks and target-specific routes. The joint priority-domain and high-to-low-timestep curriculum reduces v-NMSE, PSD-NMSE, and FC-MSE by 6.5%, 16.3%, and 10.5%, respectively, relative to uniform sampling over both dimensions, and shows strong downstream performance across six in- and out-of-domain tasks. The taskonomy reveals asymmetric, target-dependent transfer, while exploratory sealed-test evaluation shows larger descriptive gains for BIP policies when higher-order route spaces are available than for matched random controls. Together, these findings support organizing fMRI pretraining and adaptation by measured learning relations rather than treating domains and tasks as independent flat sets.

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiafan Li · Mengxue Yang · Jiaqi Zhu · Liang Chang · Ying Li · Hongan Wang
10 September 2026 at 12:00
arXiv:2608.05833v3 Announce Type: replace Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images. Traditional representation learning approaches follow the embedding-based paradigm and may struggle when relation-specific evidence is limited. Meanwhile, LLM-based reasoning methods typically linearize graph structures into textual prompts, which obscures structural topology and neglects vital visual information. While vision-language models (VLMs) excel at multimodal reasoning, they cannot natively interpret structured graph topology, particularly when it comes to knowledge graphs where nodes and edges carry complex semantics. To bridge this gap, we propose ViSR-KGC, a visual subgraph reasoning approach for KGC. It integrates three complementary capabilities to capture semantic correlations: identifying global topology dependencies via representation learning, analyzing local multimodal evidence using VLMs, and providing necessary commonsense knowledge inherent in pre-trained models. Based on learned multimodal embeddings, our framework first extracts a compact and query-aware subgraph from the MMKG. Then, this subgraph is transformed into a visually interpretable image using a layout strategy selected through empirical comparison. Finally, the visualized subgraph, entity images, textual descriptions, and candidate answers are combined into a unified prompt, enabling the VLM to infer the missing entity.

Lipotoxicity-induced ER-mitochondrial hypercoupling activates the mtDNA-cGAS-STING-NF-κB axis to drive follicular arrest in metabolically compromised PCOS

✇Cell Death Discovery nature.com science feeds
By: Mingrui Xue · Qi Zhu · Shuli Zhang · Guangzong Zhao · Xiaoqi Hong · Di Pang · Yue Xu · Xiaolu Li · Yuxin Liu · Ying Li · Qiucheng Jia · Sisi Xu · Hongshan Ge
8 September 2026 at 08:00

Cell Death Discovery, Published online: 08 September 2026; doi:10.1038/s41420-026-03339-w

Lipotoxicity-induced ER-mitochondrial hypercoupling activates the mtDNA-cGAS-STING-NF-κB axis to drive follicular arrest in metabolically compromised PCOS

Wuwei Huanglian Wan inhibits Helicobacter pylori and alleviates associated gastritis: host metabolic remodeling and altered IL-6/STAT3 signaling

✇Omics in Gastric
By: Wenxi Yang · Gengyuan Yu · Yanan Zhang · Dongsheng Ren · Yuying Li · Jin Yang · Meicun Yao
5 September 2026 at 18:00

J Ethnopharmacol. 2026 Sep 5;374(Pt 1):122370. doi: 10.1016/j.jep.2026.122370. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Wuwei Huanglian Wan (WWHLW) is a traditional Tibetan medicine formula developed by Tibetan physician Takpe Pingcuo and officially documented in the Ministry of Health Drug Standards for Tibetan Medicines (Volume I, 1995). It has long been used for the treatment of gastrointestinal disorders, particularly conditions associated with gastrointestinal discomfort and inflammation. Given the overlap between its traditional indications and the clinical manifestations of H. pylori-associated gastritis (HAG), WWHLW represents a promising candidate for the management of H. pylori infection and related gastric inflammation. In addition, several constituent herbs of WWHLW have demonstrated anti-H. pylori and anti-inflammatory activities, providing a pharmacological basis for further investigating its therapeutic effects.

AIM OF THE STUDY: This study aimed to systematically evaluate the therapeutic effects of WWHLW against H. pylori infection and HAG, and to explore the biological processes associated with these effects through integrated multi-omics and experimental validation.

MATERIALS AND METHODS: The therapeutic effects of WWHLW were evaluated through in vitro antibacterial assays and an H. pylori-infected mouse model. UHPLC-HRMS/MS was employed for chemical profiling and identification of serum-absorbed constituents. Serum metabolomics, 16S rRNA gene sequencing, network pharmacology analysis, molecular docking analysis, and molecular biological analyses were integrated to investigate the metabolic, microbial, and signaling changes associated with its therapeutic activity.

RESULTS: WWHLW exhibited anti-H. pylori activity, with minimum inhibitory concentrations (MICs) of 0.2-0.5 mg/mL against both standard strains and multidrug-resistant clinical isolates. At MIC concentrations, WWHLW treatment altered the expression of multiple virulence-associated genes and reduced gastric H. pylori colonization by 93.8% in infected mice. UHPLC-HRMS/MS analysis putatively annotated 121 compounds in the WWHLW extracts, of which 10 prototype constituents were detected in serum after oral administration. Integrated metabolomics and network pharmacology analyses revealed alterations in lipid and amino acid-related metabolic pathways following WWHLW treatment. Gut microbiota analysis showed that WWHLW was associated with less pronounced alterations in microbial diversity and composition than antibiotic treatment. Correlation analysis further revealed statistical associations between microbial taxa and lipid and amino acid-related features. Experimental validation showed that WWHLW reduced inflammatory cytokine expression and suppressed STAT3 phosphorylation, consistent with altered IL-6/STAT3-related molecular changes.

CONCLUSIONS: WWHLW exhibits therapeutic potential against H. pylori infection and HAG through combined antibacterial, anti-inflammatory and metabolic regulatory effects. The protective activity of WWHLW was associated with reduced IL-6/STAT3 signaling, providing pharmacological evidence supporting its traditional use in gastrointestinal disorders.

PMID:42700849 | DOI:10.1016/j.jep.2026.122370

Received — 27 May 2026 ⏭

Scaling up Energy-Aware Multi-Agent Reinforcement Learning for Mission-Oriented Drone Networks with Individual Reward

✇cs.AI, q-bio.NC updates on arXiv.org
By: Changling Li · Ying Li
26 May 2026 at 12:00
arXiv:2605.24992v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) has shown wide applicability in collaborative systems such as autonomous driving and smart cities for its ability of learning through interaction. With the recent development of drone networks, researchers have also applied MARL to address the trajectory planning problems. However, the dynamic environment and the limited battery capacity are still challenging for using MARL to achieve efficient collaborative task execution. In this paper, we propose an energy-aware MARL model as an attempt to tackle these challenges, leveraging Deep Q-Networks (DQN) with \emph{individual reward functions} driven by the task execution progress and the remaining battery of drones. We conduct a set of simulation studies for the proposed mode and compare it with the shared reward MARL~\cite{Li2022MARL} to explore the impact of credit assignment in MARL. The results indicate that our proposed model can achieve at least 80\% success rate regardless of the task locations and lengths. Similar to the shared reward mode, the individual reward mode can achieve a better success rate when the task density is high, and it can hit nearly a 100\% success rate when task density gets close to 40\%. The true advantage of our proposed model with individual reward is revealed when scaling up the environment. The comparison to the shared reward MARL shows that the our proposed model is more robust towards the change of the environment size and agent numbers. It can achieve higher success rate with fewer steps due to the clarity of the goal which improves energy efficiency even better.

Multi-omics Analysis Reveals the Protection of a Quadruple Probiotic Mixture in Experimental Autoimmune Hepatitis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Yu Chen · Shuhui Wang · Anzhuo Chen · Zhuoying Lin · Han Wang · Wanneng Li · Jun Liu · Jia Yao · Dean Tian · Yu Lei · Mei Liu
23 May 2026 at 18:00

Probiotics Antimicrob Proteins. 2026 May 23. doi: 10.1007/s12602-026-11062-2. Online ahead of print.

ABSTRACT

Autoimmune hepatitis (AIH) is a chronic progressive inflammatory liver disease with a rising global incidence. The treatment of AIH remains challenging because first-line drugs show limited efficacy and systemic side effects. Gut microbiota plays a crucial role in the pathogenesis of AIH, leading to growing interest in developing probiotic-based therapies. In this study, we used multi-omics analysis to investigate the therapeutic effects of a quadruple probiotic mixture (Probiotic-quad) consisting of Bifidobacterium infantis, Lactobacillus acidophilus, Enterococcus faecalis, and Bacillus cereus in a well-established chronic AIH murine model. Our results showed that Probiotic-quad treatment significantly alleviated AIH progression, as evidenced by lower serum liver enzyme levels, ameliorated hepatic inflammatory infiltration and histopathological damage. Metagenomic sequencing results showed that gut dysbiosis in AIH mice was partially reversed after Probiotic-quad administration. Additionally, the integrity of the intestinal epithelial barrier was restored, accompanied by a reduction in serum lipopolysaccharide levels. Untargeted metabolomic and transcriptomic analysis revealed that Probiotic-quad treatment was linked to alterations in hepatic metabolism, including the citrate cycle and tryptophan metabolism, and was associated with reduced activation of the NF-κB and NOD-like receptor signaling pathways. These findings suggest that Probiotic-quad treatment ameliorates AIH severity and is potentially associated with changes in hepatic immune responses, metabolism, gut microbiota, and intestinal barrier function, highlighting its potential as an adjuvant therapy for AIH.

PMID:42176246 | DOI:10.1007/s12602-026-11062-2

A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence

✇Nature - Issue - nature.com science feeds
By: Min He · Jia Su · Xiaogang Zhou · Tuo Qi · Jiazheng Wang · Tianxin Zhang · Jinhua Chen · Mawsheng Chern · Youpin Xu · Xiang Lu · Qingqing Hou · Hongrui Liu · Wenfang Huang · Jiawei Liu · Shiying Li · Yunfei Zhu · Xue Chen · Senyu Ran · Han Li · Renju Liu · Mingliang Lei · Guosong Shu · Haicheng Liao · Junjie Yin · Xiaobo Zhu · Yongyan Tang · Li Song · Long Wang · Qing Xiong · Jiali Liu · Yu Bi · Yihua Yang · Hai Qing · Bingtian Ma · Yan Li · Jing Fan · Peng Qin · Yuping Wang · Jianming Wu · Wenming Wang · Shigui Li · Weitao Li · Jing Wang · Xuewei Chen
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10572-x

A fungal long non-coding RNA from Magnaporthe oryzae translocates into rice cells to sequester a host microRNA that normally represses PKR1, a negative immunity regulator, thereby facilitating infection and revealing a widespread RNA-based pathogen–host interaction mechanism.

Induction of broadly neutralizing HIV antibodies by a two-step mechanism informs vaccine design

✇AAAS: Table of Contents
By: Ashwin N. Skelly · Harry B. Gristick · Hui Li · Edem Gavor · Andrew J. Connell · Edward F. Kreider · Lorie Marchitto · Michael P. Hogarty · Maddy L. Newby · Joel D. Allen · Weimin Liu · Anthony P. West · Kasirajan Ayyanathan · Mary S. Campion · Kaitlyn Winters · Colette G. Gordon · Rebecca A. Osbaldeston · Macy J. Akeley · Emily Lewis · Yingying Li · Ajay Singh · Kendra Cruickshank · Younghoon Park · Chengyan Zhao · Xuduo Li · Khaled Amereh · Elizabeth Van Itallie · John W. Carey · Amie Albertus · Andrew T. DeLaitsch · Jennifer R. Keeffe · Melinda G. Lituchy · Agnes A. Walsh · Daniel J. Morris · Rumi Habib · Frederic Bibollet-Ruche · Nitesh Mishra · Gabriel Avillion · Nicholas S. Koranda · Samantha J. Plante · Christian L. Martella · Jinery Lora · Eric J. D. Wang · Mark G. Lewis · Malcolm A. Martin · Michel C. Nussenzweig · Michael S. Seaman · Darrell J. Irvine · Kevin J. Wiehe · Barton F. Haynes · Kshitij Wagh · Bette Korber · Raiees Andrabi · Max Crispin · Drew Weissman · Pamela J. Bjorkman · Beatrice H. Hahn · George M. Shaw
7 May 2026 at 13:59
Science, Ahead of Print.

GPNMB Drives Brain Metastasis by Sculpting a Pathological Endothelial-Immune Interactome

✇Omics In Lung
By: Xuefei Liu · Jun Tan · Chun Wu · Guanyin Huang · Yixin Cheng · Jianyang Hu · Binyu Zhang · Mao Zhao · Boxi Zhao · Jingru Lian · Shuqian Zheng · Lin Zeng · Meng Xu · Yang Xu · Shan Zeng · Hao Yu · Hui Yang · Zhixiang Zuo · Chuanyu Liu · Weineng Feng · Weinan Guo · Chunying Li · Sai-Lan Liu · Qing Liu · Feiqiu Wen · Xin Hong
13 April 2026 at 18:00

Cancer Discov. 2026 Apr 15. doi: 10.1158/2159-8290.CD-25-1663. Online ahead of print.

ABSTRACT

Brain metastases (BM) remain a devastating disease with dismal prognosis. How circulating tumor cells (CTCs) penetrate the blood brain barrier (BBB) and reprogram the brain microenvironment remain unclear. Using spatially resolved multi-omic profiling of CTCs and brain metastases, integrated with experimental and clinical analyses, we identified Glycoprotein Non-Metastatic Melanoma Protein B (GPNMB) as a CTC-secreted driver of vascular disruption and brain colonization. CBX3 upregulation induced GPNMB expression, which bound endothelial EGFR, triggering CBL-mediated ubiquitination and degradation. Attenuated EGFR signaling suppressed FTO and disrupted endothelial junctions via YTHDF2-dependent TJP1 m6A methylation. Remarkably, GPNMB-induced BBB remodeling promoted immune infiltration via CXCL12-CXCR4 axis, and induced time course-dependent T cell exhaustion within the brain microenvironment. Clinically, elevated CBX3⁺GPNMB⁺ CTCs and plasma CXCL12 were significantly associated with BM progression in lung cancer and melanoma. Therapeutically, dual blockade of GPNMB and PD1 enhanced anti-BM efficacy in mice, unveiling GPNMB as a promising target for precision immunotherapy.

PMID:41973996 | DOI:10.1158/2159-8290.CD-25-1663

Transplantation of encapsulated mitochondria alleviates dysfunction in mitochondrial and Parkinson’s disease models

✇Cell
By: Shiwei Du · Qi Long · Yanshuang Zhou · Jiangqin Fu · Hao Wu · Liang Yang · Yaohang Xie · Yingzhe Ding · Maolei Zhang · Jingyi Guo · Mengfei Wang · Jiajun Lin · Mingli Hu · Jian Zhang · Deyang Yao · Wei Li · Feixiang Bao · Ge Xiang · Yi Wu · Yile Huang · Haozhao Liang · Rui Wang · Heying Li · Baodan Chen · Chong Li · Junwei Wang · Jiwei Zhang · Dajiang Qin · Jianwei Sun · Yun Zhu · Fei Sun · Wuming Wang · Gang Lu · Wai-Yee Chan · Hui Zhao · Chenli Liu · Xingguo Liu
18 March 2026 at 08:00
A mitochondrial transplantation approach rescues mitochondrial deficiency and prevents mitochondrial DNA depletion syndrome, Leigh syndrome, and Parkinson’s disease in cellular and mouse models.

Respiratory viral infections prime accelerated lung cancer growth

✇Cell
By: Wei Qian · Xiaoqin Wei · Andrew J. Barros · Xiangyu Ye · Haibo Zhang · Qing Yu · Samuel P. Young · Eric V. Yeatts · Yury Park · Chaofan Li · Sijie Hao · Gislane Almeida-Santos · Jinyi Tang · Harish Narasimhan · Nicole A. Kirk · Valeria Molinary · Ying Li · Li Li · Bimal N. Desai · Peter Chen · Kwon-Sik Park · Anny Xiaobo Zhou · Jeffrey M. Sturek · Wei Chen · In Su Cheon · Jie Sun
11 March 2026 at 08:00
Severe COVID-19 is associated with an increased subsequent risk of lung cancer. Viral pneumonia induces durable lung epigenetic imprinting that promotes tumor-supportive neutrophils and impairs T cell immunity, which is reversible with combined CXCR2 inhibition and PD-L1 blockade.
Received — 13 April 2026 ⏭

Engineered immunosuppressive dendritic cells protect against cardiac remodelling

✇Nature - Issue - nature.com science feeds
By: Xiaoying Li · Jiamin Li · Guohua Li · Lisheng Zhu · Guo Cheng · Huanqiang Li · Hao Lin · Ningqing Jia · Xiaoqian Hong · Ye Liu · Zhiwei Zhong · Yize Chen · Biqing Wang · Jing Zhao · Zhenqi Hua · Lingjun Wang · Qiming Chen · Peijie Zheng · Shuyuan Sheng · Songting Gu · Cheng Ni · Shuchang Ye · Changle Ke · Feimu Zhang · Mo Li · Shaohui Shi · Junhua He · Yan Wu · Yinghui Xu · Minjian Kong · Qi Chen · Huajun Li · Yu Zhang · Jianzhong Sun · Guanhua Hu · Chengchen Zhao · Yiping Dong · Lili Yu · Yang Xu · Xinyang Hu
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10346-5

Lesion-targeted immune modulation is a feasible strategy to control cardiac fibrosis, and engineered dendritic cells are a promising therapeutic platform for treating cardiac remodelling and heart failure.

Superconductivity and electronic structures of nickelate thin film superstructures

✇Nature - Issue - nature.com science feeds
By: Zihao Nie · Yueying Li · Wei Lv · Lizhi Xu · Zhicheng Jiang · Peng Fu · Guangdi Zhou · Wenhua Song · Yaqi Chen · Heng Wang · Haoliang Huang · Junhao Lin · Jin-Feng Jia · Dawei Shen · Peng Li · Qi-Kun Xue · Zhuoyu Chen
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10352-7

Engineered Ruddlesden–Popper nickelate superstructures show that specific Fermi surface features enable ambient-pressure superconductivity, linking structural configuration, electronic structure and superconducting behaviour. .
Received — 6 April 2026 ⏭

Deciphering functional intra-tumoral heterogeneity in BRAF<sup>V600E</sup>-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling

✇Oncogene - Issue - nature.com science feeds
By: Jing Yu · Siyi Shen · Rongkui Luo · Tianyuan Qiu · Jingjing Chen · Wenqing Kong · Ruisen Ma · Ying Yan · Zhoumin Niu · Mei Ma · Zhuoyang Li · Yuting Wu · Yuying Li · Ling Zhang · Hao Ying
4 April 2026 at 08:00

Oncogene, Published online: 04 April 2026; doi:10.1038/s41388-026-03742-8

Deciphering functional intra-tumoral heterogeneity in BRAFV600E-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling
Received — 4 April 2026 ⏭

M3D-BFS: a Multi-stage Dynamic Fusion Strategy for Sample-Adaptive Multi-Modal Brain Network Analysis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rui Dong · Xiaotong Zhang · Jiaxing Li · Yueying Li · Jiayin Wei · Youyong Kong
3 April 2026 at 12:00
arXiv:2604.01667v1 Announce Type: new Abstract: Multi-modal fusion is of great significance in neuroscience which integrates information from different modalities and can achieve better performance than uni-modal methods in downstream tasks. Current multi-modal fusion methods in brain networks, which mainly focus on structural connectivity (SC) and functional connectivity (FC) modalities, are static in nature. They feed different samples into the same model with identical computation, ignoring inherent difference between input samples. This lack of sample adaptation hinders model's further performance. To this end, we innovatively propose a multi-stage dynamic fusion strategy (M3D-BFS) for sample-adaptive multi-modal brain network analysis. Unlike other static fusion methods, we design different mixture-of-experts (MoEs) for uni- and multi-modal representations where modules can adaptively change as input sample changes during inference. To alleviate issue of MoE where training of experts may be collapsed, we divide our method into 3 stages. We first train uni-modal encoders respectively, then pretrain single experts of MoEs before finally finetuning the whole model. A multi-modal disentanglement loss is designed to enhance the final representations. To the best of our knowledge, this is the first work for dynamic fusion for multi-modal brain network analysis. Extensive experiments on different real-world datasets demonstrates the superiority of M3D-BFS.
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