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

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

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Thinking in Streaming Video

arXiv:2603.12938v1 Announce Type: cross Abstract: Real-time understanding of continuous video streams is essential for interactive assistants and multimodal agents operating in dynamic environments. However, most existing video reasoning approaches follow a batch paradigm that defers reasoning until the full video context is observed, resulting in high latency and growing computational cost that are incompatible with streaming scenarios. In this paper, we introduce ThinkStream, a framework for streaming video reasoning based on a Watch--Think--Speak paradigm that enables models to incrementally update their understanding as new video observations arrive. At each step, the model performs a short reasoning update and decides whether sufficient evidence has accumulated to produce a response. To support long-horizon streaming, we propose Reasoning-Compressed Streaming Memory (RCSM), which treats intermediate reasoning traces as compact semantic memory that replaces outdated visual tokens while preserving essential context. We further train the model using a Streaming Reinforcement Learning with Verifiable Rewards scheme that aligns incremental reasoning and response timing with the requirements of streaming interaction. Experiments on multiple streaming video benchmarks show that ThinkStream significantly outperforms existing online video models while maintaining low latency and memory usage. Code, models and data will be released at https://github.com/johncaged/ThinkStream
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