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Received β€” 15 September 2026 ⏭

Integrated Metabolomic and Transcriptomic Analysis Suggests Potential Therapeutic Mechanism of Shengxian Decoction in Hypobaric Hypoxia-Induced Pulmonary Hypertension in SD Rats

βœ‡Omics In Lung
By: Yue Lin Β· Junjun Han Β· Guiqin Liu Β· Kang'an Cheng Β· Xiangyang Li Β· Ming Ren
10 September 2026 at 18:00

Drug Des Devel Ther. 2026 Sep 5;20:603123. doi: 10.2147/DDDT.S603123. eCollection 2026.

ABSTRACT

BACKGROUND: High-altitude hypoxia can trigger maladaptive cardiopulmonary responses, with hypoxia-induced pulmonary hypertension (HPH) representing a major clinical challenge with limited therapeutic options. Shengxian Decoction (SXT), a classical traditional Chinese medicine formula for treating "qi deficiency and sinking", has shown clinical benefits, but the molecular pathways associated with its effects remain incompletely understood.

METHODS: Male Sprague-Dawley rats were exposed to simulated high altitude (5000 m; 404 mmHg, 10.8% O2) for 28 days and treated with SXT at three doses (1.8, 3.6, or 7.2 g/kg/day; n = 6/group). Integrated serum metabolomics (UHPLC-Q-TOF-MS) and lung transcriptomics (RNA-seq) were applied. Multivariate analysis, pathway enrichment, weighted gene co-expression network analysis, and cross-omics correlation were used for data integration. After randomization, allocation concealment and blinding were strictly implemented throughout all experimental procedures, with all interventions and outcome assessments performed by personnel blinded to group assignment until completion of data analysis.

RESULTS: Chronic hypoxia induced HPH with elevated mPAP, RVHI, RVWI and pulmonary vascular remodeling (increased WT% and WA%), while SXT dose-dependently ameliorated these abnormalities and restored hypoxia-disrupted metabolomic and transcriptomic profiles, with the high-dose group showing the most pronounced effect. Chronic hypoxia induced pronounced metabolic and transcriptional remodeling, with model animals clearly separated from controls in principal component analysis. Most differentially expressed genes exhibited downregulated expression, indicating global transcriptional suppression. SXT treatment dose-dependently restored both metabolomic and transcriptomic profiles, with the high-dose group most closely resembling controls. These pyruvate-proximal nodes may represent potential points of convergence through which SXT-associated metabolic and transcriptional alterations are coordinated. The relationships reported here are based on cross-omics associations, and causal inference will require further functional validation.

CONCLUSION: These findings suggest that SXT may ameliorate HPH partly through coordinated regulation of metabolic pathways and gene networks, particularly those related to energy metabolism, rather than fully explaining disease pathogenesis. The study provides multi-omics evidence supporting the traditional concept of "replenishing qi and elevating sunken qi" and identifies candidate metabolic biomarkers for further investigation. However, the results should be interpreted cautiously because of the relatively small sample size, the lack of functional validation experiments, and the exploratory nature of the biomarker findings. Further mechanistic and clinical studies are required to confirm these observations.

PMID:42719424 | PMC:PMC13557172 | DOI:10.2147/DDDT.S603123

Received β€” 27 May 2026 ⏭

SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Junli Liang Β· Pengfei Zhou Β· Wangqiu Zhou Β· Wenjie Qing Β· Qi Zhao Β· Ziwen Wang Β· Qi Song Β· Xiangyang Li
26 May 2026 at 12:00
arXiv:2601.03014v3 Announce Type: replace-cross Abstract: Traditional Retrieval-Augmented Generation (RAG) effectively supports single-hop question answering with large language models but faces significant limitations in multi-hop question answering tasks, which require combining evidence from multiple documents. Existing chunk-based retrieval often provides irrelevant and logically incoherent context, leading to incomplete evidence chains and incorrect reasoning during answer generation. To address these challenges, we propose SentGraph, a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering. Specifically, we construct a hierarchical sentence graph offline by first adapting Rhetorical Structure Theory to distinguish nucleus and satellite sentences, and then organizing them into topic-level subgraphs with cross-document entity bridges. During online retrieval, SentGraph performs graph-guided evidence selection and path expansion to retrieve fine-grained sentence-level evidence. Extensive experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of SentGraph, validating the importance of explicitly modeling sentence-level logical dependencies for multi-hop reasoning.
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