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Received — 13 April 2026 ⏭

Integrin β3 deficiency unleashes spontaneous pulmonary inflammation by promoting B cell hyperactivation via the CD40-CD40L axis

✇Omics In Lung
By: Shuang Liu · Kaiwen Wang · Jiayi Chen · Yan Shen · Zheng Ruan · Kankan Wang · Xiaodong Xi · Jianhua Mao
9 April 2026 at 18:00

Front Immunol. 2026 Mar 24;17:1796926. doi: 10.3389/fimmu.2026.1796926. eCollection 2026.

ABSTRACT

BACKGROUND: Pulmonary immune homeostasis requires tight control of adaptive responses. Integrin β3 is a well-known mediator of cell adhesion and platelet function. However, its role in adaptive immunity, especially in B cell responses, remains unclear.

METHODS: We defined the pulmonary phenotype of constitutive β3-deficient (β3-/-) mice by histopathology. We performed integrated transcriptomic and proteomic profiling of lung tissue to map the molecular signature of spontaneous pulmonary inflammation. We further probed the underlying mechanisms with additional histology and functional assays and tested for biological significance using transcriptomics data from auto-immune disease patients.

RESULTS: β3-/- mice developed spontaneous pulmonary inflammation marked by B cell activation and in situ immune-complex deposition within alveoli. Multi-omics integration implicated the CD40-CD40 Ligand (CD40L) axis as a central driver of this pathology. Mechanistically, loss of β3 enhanced CD40L-CD40 engagement on B cells, resulting in NF-κB pathway hyperactivation. Consistent with our murine data, reduced ITGB3 expression in patients with autoimmune disease correlated with transcriptional signatures of B cell activation and inflammation.

CONCLUSIONS: These results reframe integrin β3 as a threshold regulator of B cell activation. The β3-CD40L-CD40 axis therefore represents a potential therapeutic target for B cell-mediated autoimmune diseases.

PMID:41953039 | PMC:PMC13055533 | DOI:10.3389/fimmu.2026.1796926

Integrin β3 deficiency unleashes spontaneous pulmonary inflammation by promoting B cell hyperactivation via the CD40-CD40L axis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Shuang Liu · Kaiwen Wang · Jiayi Chen · Yan Shen · Zheng Ruan · Kankan Wang · Xiaodong Xi · Jianhua Mao
9 April 2026 at 18:00

Front Immunol. 2026 Mar 24;17:1796926. doi: 10.3389/fimmu.2026.1796926. eCollection 2026.

ABSTRACT

BACKGROUND: Pulmonary immune homeostasis requires tight control of adaptive responses. Integrin β3 is a well-known mediator of cell adhesion and platelet function. However, its role in adaptive immunity, especially in B cell responses, remains unclear.

METHODS: We defined the pulmonary phenotype of constitutive β3-deficient (β3-/-) mice by histopathology. We performed integrated transcriptomic and proteomic profiling of lung tissue to map the molecular signature of spontaneous pulmonary inflammation. We further probed the underlying mechanisms with additional histology and functional assays and tested for biological significance using transcriptomics data from auto-immune disease patients.

RESULTS: β3-/- mice developed spontaneous pulmonary inflammation marked by B cell activation and in situ immune-complex deposition within alveoli. Multi-omics integration implicated the CD40-CD40 Ligand (CD40L) axis as a central driver of this pathology. Mechanistically, loss of β3 enhanced CD40L-CD40 engagement on B cells, resulting in NF-κB pathway hyperactivation. Consistent with our murine data, reduced ITGB3 expression in patients with autoimmune disease correlated with transcriptional signatures of B cell activation and inflammation.

CONCLUSIONS: These results reframe integrin β3 as a threshold regulator of B cell activation. The β3-CD40L-CD40 axis therefore represents a potential therapeutic target for B cell-mediated autoimmune diseases.

PMID:41953039 | PMC:PMC13055533 | DOI:10.3389/fimmu.2026.1796926

Received — 17 February 2026 ⏭

Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yifan Sun · Jingyan Shen · Yibin Wang · Tianyu Chen · Zhendong Wang · Mingyuan Zhou · Huan Zhang
17 February 2026 at 13:00
arXiv:2506.05316v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become an effective approach for fine-tuning large language models (LLMs), particularly to enhance their reasoning capabilities. However, RL fine-tuning remains highly resource-intensive, and existing work has largely overlooked the problem of data efficiency. In this paper, we propose two techniques to improve data efficiency in LLM RL fine-tuning: difficulty-targeted online data selection and rollout replay. We introduce the notion of adaptive difficulty to guide online data selection, prioritizing questions of moderate difficulty that are more likely to yield informative learning signals. To estimate adaptive difficulty efficiently, we develop an attention-based framework that requires rollouts for only a small reference set of questions. The adaptive difficulty of the remaining questions is then estimated based on their similarity to this set. To further reduce rollout cost, we introduce a rollout replay mechanism inspired by experience replay in traditional RL. This technique reuses recent rollouts, lowering per-step computation while maintaining stable updates. Experiments across 6 LLM-dataset combinations show that our method reduces RL fine-tuning time by 23% to 62% while reaching the same level of performance as the original GRPO algorithm. Our code is available at https://github.com/ASTRAL-Group/data-efficient-llm-rl.
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