Integrated Multi-Omics Analysis Reveals Modulation of the Ras Pathway by Siji Kangbingdu Mixture in Acute Lung Injury

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Bo Han · Fei Luan · Xiaofei Zhang · Dongyan Guo · Bingtao Zhai · Jing Sun · Changli Wang · Chunli Cui · Junbo Zou · Yajun Shi

14 March 2026 at 18:00

Comb Chem High Throughput Screen. 2026 Mar 11. doi: 10.2174/0113862073398293251205055042. Online ahead of print.

ABSTRACT

INTRODUCTION: This study aimed to investigate the protective effects of Siji Kangbingdu Mixture (SKM) against acute lung injury (ALI) in mice and to elucidate its underlying mechanisms.

METHODS: ALI was induced in Kunming mice via intranasal administration of LPS (5 mg/kg), followed by oral SKM treatment for 7 days. Lung wet-to-dry (W/D) ratio, histopathology, multiomics analysis, and network pharmacology were performed. Key targets and pathways were identified through dynamic KEGG analysis and validated by Western blotting.

RESULTS: SKM treatment ameliorated alveolar hemorrhage, alveolar wall disruption, septal thickening, edema, and inflammatory cell infiltration. Integrated multi-omics analysis revealed that SKM primarily modulated the Ras signaling pathway, reducing the protein expression of Phospho- MEK1/2, Raf1, Phospho-ERK1/2, and RASH/RASK/RASN, thereby contributing to the treatment of ALI.

DISCUSSION: SKM alleviated LPS-induced ALI in mice by inhibiting the Ras pathway, highlighting the pathway's role in ALI pathogenesis. However, due to limitations of the animal model and incomplete validation, further studies combining clinical research and in vitro experiments are needed to confirm its efficacy and mechanism.

CONCLUSIONS: SKM shows potential to ameliorate ALI by suppressing inflammatory responses and reducing local tissue fibrosis. The combination of metabolomics, transcriptomics, and network pharmacology elucidated its mechanism, while Western blot analysis suggested that its therapeutic effect is associated with downregulation of the Ras signaling pathway.

PMID:41830142 | DOI:10.2174/0113862073398293251205055042

Integrated Multi-Omics Analysis Reveals Modulation of the Ras Pathway by Siji Kangbingdu Mixture in Acute Lung Injury

Omics In Lung

By: Bo Han · Fei Luan · Xiaofei Zhang · Dongyan Guo · Bingtao Zhai · Jing Sun · Changli Wang · Chunli Cui · Junbo Zou · Yajun Shi

14 March 2026 at 18:00

Comb Chem High Throughput Screen. 2026 Mar 11. doi: 10.2174/0113862073398293251205055042. Online ahead of print.

ABSTRACT

INTRODUCTION: This study aimed to investigate the protective effects of Siji Kangbingdu Mixture (SKM) against acute lung injury (ALI) in mice and to elucidate its underlying mechanisms.

METHODS: ALI was induced in Kunming mice via intranasal administration of LPS (5 mg/kg), followed by oral SKM treatment for 7 days. Lung wet-to-dry (W/D) ratio, histopathology, multiomics analysis, and network pharmacology were performed. Key targets and pathways were identified through dynamic KEGG analysis and validated by Western blotting.

RESULTS: SKM treatment ameliorated alveolar hemorrhage, alveolar wall disruption, septal thickening, edema, and inflammatory cell infiltration. Integrated multi-omics analysis revealed that SKM primarily modulated the Ras signaling pathway, reducing the protein expression of Phospho- MEK1/2, Raf1, Phospho-ERK1/2, and RASH/RASK/RASN, thereby contributing to the treatment of ALI.

DISCUSSION: SKM alleviated LPS-induced ALI in mice by inhibiting the Ras pathway, highlighting the pathway's role in ALI pathogenesis. However, due to limitations of the animal model and incomplete validation, further studies combining clinical research and in vitro experiments are needed to confirm its efficacy and mechanism.

CONCLUSIONS: SKM shows potential to ameliorate ALI by suppressing inflammatory responses and reducing local tissue fibrosis. The combination of metabolomics, transcriptomics, and network pharmacology elucidated its mechanism, while Western blot analysis suggested that its therapeutic effect is associated with downregulation of the Ras signaling pathway.

PMID:41830142 | DOI:10.2174/0113862073398293251205055042

BoxMind: Closed-loop AI strategy optimization for elite boxing validated in the 2024 Olympics

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

By: Kaiwen Wang · Kaili Zheng · Rongrong Deng · Qingmin Fan · Milin Zhang · Zongrui Li · Xuesi Zhou · Bo Han · Liren Chen · Chenyi Guo · Ji Wu

10 March 2026 at 12:00

arXiv:2601.11492v2 Announce Type: replace Abstract: Competitive sports require sophisticated tactical analysis, yet combat disciplines like boxing remain underdeveloped in AI-driven analytics due to the complexity of action dynamics and the lack of structured tactical representations. To address this, we present BoxMind, a closed-loop AI expert system validated in elite boxing competition. By defining atomic punch events with precise temporal boundaries and spatial and technical attributes, we parse match footage into 18 hierarchical technical-tactical indicators. We then propose a graph-based predictive model that fuses these explicit technical-tactical profiles with learnable, time-variant latent embeddings to capture the dynamics of boxer matchups. Modeling match outcome as a differentiable function of technical-tactical indicators, we turn winning probability gradients into executable tactical adjustments. Experiments show that the outcome prediction model achieves state-of-the-art performance, with 69.8% accuracy on BoxerGraph test set and 87.5% on Olympic matches. Using this predictive model as a foundation, the system generates strategic recommendations that demonstrate proficiency comparable to human experts. BoxMind is validated through a closed-loop deployment during the 2024 Paris Olympics, directly contributing to the Chinese National Team's historic achievement of three gold and two silver medals. BoxMind establishes a replicable paradigm for transforming unstructured video data into strategic intelligence, bridging the gap between computer vision and decision support in competitive sports. Code and data is available at https://github.com/gouba2333/BoxingWeb.

KVCache Cache in the Wild: Characterizing and Optimizing KVCache Cache at a Large Cloud Provider

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

By: Jiahao Wang · Jinbo Han · Xingda Wei · Sijie Shen · Dingyan Zhang · Chenguang Fang · Rong Chen · Wenyuan Yu · Haibo Chen

17 February 2026 at 13:00

arXiv:2506.02634v5 Announce Type: replace-cross Abstract: Serving large language models (LLMs) is important for cloud providers, and caching intermediate results (KV\$) after processing each request substantially improves serving throughput and latency. However, there is limited understanding of how LLM serving benefits from KV\$ caching, where system design decisions like cache eviction policies are highly workload-dependent. In this paper, we present the first systematic characterization of the KV\$ workload patterns from one of the leading LLM service providers. We draw observations that were not covered by previous studies focusing on synthetic workloads, including: KV\$ reuses are skewed across requests, where reuses between single-turn requests are equally important as multi-turn requests; the reuse time and probability are diverse considering all requests, but for a specific request category, the pattern tends to be predictable; and the overall cache size required for an ideal cache hit ratio is moderate. Based on the characterization, we further propose a workload-aware cache eviction policy that improves the serving performance under real-world traces, especially with limited cache capacity.

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