Inhaled siRNA therapy targeting RAGE for pulmonary inflammation: a first-in-human randomized trial

By: Mark O’Carroll · David Kasahara · John Huetsch · Taylor Reed · Timothy N. Perkins · Rong Zhou · Lindsey Moser · Bianca Tomasini-Johansson · Anh Ta · Tim D. Oury · Tao Pei · James Hamilton · Erik W. Bush · Nicholas J. Leeper · Matthias Salathe · John Blakely · Adrian Barnett · Eung gu Lee · Sang Heon Kim · YongChul Lee · Michelle Baker · Paul Hamilton · Iwona Kobielsz-Gembala · Zenon Siergiejko · Elzbieta Hajol · Mara Rachel · Juan Roldan Sanchez · Joaquin Sastre · David Romero · Kittipong Maneechotesuwan · Watchara Boonsawat · Kittima Bangpattanasiri · Nadwipa Yuangtrakul

9 September 2026 at 08:00

Nature Medicine, Published online: 09 September 2026; doi:10.1038/s41591-026-04607-z

Following preclinical development, a randomized first-in-human trial showed that treatment with an inhaled, lung-epithelium-targeted siRNA directed against the receptor for advanced glycation end-products (RAGE) was safe and well tolerated and reduced RAGE levels in serum and bronchoalveolar lavage fluid.

Integrated transcriptomic and immunogenomic analysis unravels the immunological functions and prognostic landscape of WD repeat domain 76

Omics in Hepatocellular

By: Yifan Wang · Rong Zhou · Shenglong Guo · Shaoting Nan · Yanlong Duan · Lei Cao · Xiujuan Huang · Hailong Yan

3 September 2026 at 18:00

Int J Immunopathol Pharmacol. 2026 Jan-Dec;40:3946320261486727. doi: 10.1177/03946320261486727. Epub 2026 Sep 3.

ABSTRACT

BackgroundWD Repeat Domain 76 (WDR76) plays a potential role in cellular regulation; however, its comprehensive landscape across human malignancies and its specific biological function in hepatocellular carcinoma (HCC) remain largely unexplored.MethodsWe conducted a systematic pan-cancer analysis utilizing multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) atabases to evaluate WDR76 expression, subcellular localization, and its correlation with clinicopathologic features, genomic instability, and immune infiltration. Diagnostic and prognostic values were assessed via Receiver operating characteristic (ROC) and Kaplan-Meier analyses. Furthermore, the functional role of WDR76 in HCC was validated in vitro using Hep-3B and Huh7 cell lines through siRNA-mediated knockdown, followed by CCK-8, wound-healing, and transwell assays.ResultsWDR76 was significantly upregulated in the majority of tumor types, including LIHC, LUAD, and COAD, while exhibiting nuclear localization. Elevated WDR76 expression correlated with advanced tumor staging, metastasis, and poor clinical outcomes across multiple cohorts, particularly in ACC, KIRP, and LIHC. ROC analysis highlighted its exceptional diagnostic precision in cancers such as GBM and LIHC. Immunologically, WDR76 expression was intricately linked to immune cell infiltration, immune checkpoint markers, and genomic instability parameters, suggesting a role in shaping the tumor microenvironment. Drug sensitivity profiling revealed that high WDR76 levels correlate with resistance to specific chemotherapeutic agents. Experimentally, silencing WDR76 in HCC cells significantly suppressed cell proliferation, migration, and invasion capabilities.ConclusionOur study establishes WDR76 as a robust pan-cancer prognostic biomarker and a potential immunotherapeutic target. Specifically, we provide experimental evidence that WDR76 functions as an oncogenic driver in liver cancer, promoting malignant phenotypes and offering a novel avenue for targeted therapeutic intervention.

PMID:42690047 | PMC:PMC13542525 | DOI:10.1177/03946320261486727

DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning

cs.AI, q-bio.NC updates on arXiv.org

By: Xirui Liu · Sihang Zhou · Yanning Hou · Rong Zhou · Haoyuan Chen · Maolin He · Siwei Wang · Hao Chen · Jian Huang

26 May 2026 at 12:00

arXiv:2605.23939v1 Announce Type: new Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different tasks. However, these knowledge types differ fundamentally: reasoning knowledge (e.g., booking a flight requires first searching for routes) is abstract and transferable across websites, while interaction knowledge (e.g., clicking the Search button at a specific coordinate on Site A) depends heavily on page-specific contexts. Existing methods store experiences uniformly. This creates a dilemma: abstract representations lose executability on concrete pages, while concrete representations fail to generalize across domains. This entanglement limits capability accumulation: on new websites, agents either fail to recognize reusable task logic due to surface-level differences or attempt infeasible actions from outdated page structures. To disentangle them, we propose DRIVE, a dual-level skill modeling framework separating historical experience into natural language reasoning skills, which capture transferable task logic, and programmatic interaction skills, grounding abstract actions to executable operations. A scene-aware coordination mechanism adaptively retrieves and invokes these dual-level skills based on task semantics. DRIVE also uses skill-level reflection to identify hierarchy-specific failure modes, enabling targeted skill library expansion and refinement. Experiments across five WebArena domains show DRIVE attains an average task success rate of 52.8%, exceeding the skill-free baseline by 7.3 percentage points. Further ablations show reasoning and interaction skills provide distinct, complementary benefits, supporting separation of transferable task logic from executable page-level operations.

Zodasiran for cholesterol and triglyceride lowering in patients with hyperlipidemia: final report of phase 1 basket trial

Nature Medicine

By: Gerald F. Watts · Russell Scott · David Sullivan · John Baker · Peter Clifton · James Hamilton · Bruce Given · Stacey Melquist · Rong Zhou · Daniel Gaudet · Ira J. Goldberg · Joshua W. Knowles · Nicholas J. Leeper · Robert A. Hegele · Christie M. Ballantyne

7 April 2026 at 08:00

Nature Medicine, Published online: 07 April 2026; doi:10.1038/s41591-026-04307-8

In a phase 1 basket trial, the small interfering RNA zodasiran, targeting ANGPTL3, lowered triglycerides in patients with severe hypertriglyceridemia and lowered both triglycerides and low-density lipoprotein cholesterol in patients with heterozygous familial hypercholesterolemia.

Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with Structured Output (SLSO) Framework

cs.AI, q-bio.NC updates on arXiv.org

By: Nanaka Hosokawa · Ryo Takahashi · Tomoya Kitano · Yukihiro Iida · Chisako Muramatsu · Tatsuro Hayashi · Yuta Seino · Xiangrong Zhou · Takeshi Hara · Akitoshi Katsumata · Hiroshi Fujita

25 March 2026 at 12:00

arXiv:2510.02001v4 Announce Type: replace-cross Abstract: Vision-language models (VLMs) such as GPT (Generative Pre-Trained Transformer) have shown potential for medical image interpretation; however, challenges remain in generating reliable radiological findings in clinical practice, as exemplified by dental pathologies. This study proposes a Self-correction Loop with Structured Output (SLSO) framework as an integrated processing methodology to enhance the accuracy and reliability of AI-generated findings for jaw cysts in dental panoramic radiographs. Dental panoramic radiographs with jaw cysts were used to implement a 10-step integrated processing framework incorporating image analysis, structured data generation, tooth number extraction, consistency checking, and iterative regeneration. The framework functioned as an external validation mechanism for GPT outputs. Performance was compared against the conventional Chain-of-Thought (CoT) method across seven evaluation items: transparency, internal structure, borders, root resorption, tooth movement, relationships with other structures, and tooth number. The SLSO framework improved output accuracy for multiple items compared to the CoT method, with the most notable improvements observed in tooth number identification, tooth movement detection, and root resorption assessment. In successful cases, consistently structured outputs were achieved after up to five regenerations. The framework enforced explicit negative finding descriptions and suppressed hallucinations, although accurate identification of extensive lesions spanning multiple teeth remained limited. This investigation established the feasibility of the proposed integrated processing methodology and provided a foundation for future validation studies with larger, more diverse datasets.

Diffusion-Guided Pretraining for Brain Graph Foundation Models

cs.AI, q-bio.NC updates on arXiv.org

By: Xinxu Wei · Rong Zhou · Lifang He · Yu Zhang

10 March 2026 at 12:00

arXiv:2602.09437v3 Announce Type: replace-cross Abstract: With the growing interest in foundation models for brain signals, graph-based pretraining has emerged as a promising paradigm for learning transferable representations from connectome data. However, existing contrastive and masked autoencoder methods typically rely on naive random dropping or masking for augmentation, which is ill-suited for brain graphs and hypergraphs as it disrupts semantically meaningful connectivity patterns. Moreover, commonly used graph-level readout and reconstruction schemes fail to capture global structural information, limiting the robustness of learned representations. In this work, we propose a unified diffusion-based pretraining framework that addresses both limitations. First, diffusion is designed to guide structure-aware dropping and masking strategies, preserving brain graph semantics while maintaining effective pretraining diversity. Second, diffusion enables topology-aware graph-level readout and node-level global reconstruction by allowing graph embeddings and masked nodes to aggregate information from globally related regions. Extensive experiments across multiple neuroimaging datasets with over 25,000 subjects and 60,000 scans involving various mental disorders and brain atlases demonstrate consistent performance improvements.

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