Multi-Omics Identification of Biomarkers for High-Altitude Pulmonary Hypertension

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

By: Zhe Chen · Linhong Pang · Yidan Zheng · Li Xu · Mingjing Tang · Ziwen Zhao · Tianyu Wang · Jin Li · Yunfei Zhou · Lin Duo · Wenlong Zhu · Zhiling Luo · Fei Li · Da Zhu

26 May 2026 at 18:00

J Cardiovasc Dev Dis. 2026 Apr 30;13(5):195. doi: 10.3390/jcdd13050195.

ABSTRACT

(1) Aim: The incidence of high-altitude pulmonary hypertension (HAPH) has risen in recent years and is expected to continue increasing; however, its diagnosis remains challenging. In this study, we employed proteomics and metabolomics to identify the proteins and metabolic biomarkers that contribute to the development of HAPH. (2) Methods: We applied integrated proteomics and metabolomics to match blood samples from 40 HAPH patients and 40 healthy controls in Yunnan's high-altitude regions to characterize molecular profiles, identify biomarkers, and develop a predictive model. (3) Results: Proteomic analysis identified four proteins (A2IPH7, K1C14, PSME2, SERPINE2) commonly dysregulated in HAPH patients from two high-altitude regions. SERPINE2 was notably downregulated and showed a negative correlation with clinical severity, which was further validated in HAPH rat lung tissues and supported by UK Biobank data for idiopathic PAH. Concurrent metabolomics uncovered 11 shared metabolites, largely acyl fatty acids, enriched in pathways such as unsaturated fatty acid synthesis. Integration of these multi-omics data enabled the development of a robust predictive model. (4) Conclusion: Our study identified key protein and metabolic biomarkers involved in HAPH development, which were validated in animal models. Based on these findings, a predictive model was developed, highlighting SERPINE2 and 11 metabolites as promising targets for the prediction and prevention of HAPH.

PMID:42188081 | DOI:10.3390/jcdd13050195

Multi-Omics Identification of Biomarkers for High-Altitude Pulmonary Hypertension

Omics In Lung

By: Zhe Chen · Linhong Pang · Yidan Zheng · Li Xu · Mingjing Tang · Ziwen Zhao · Tianyu Wang · Jin Li · Yunfei Zhou · Lin Duo · Wenlong Zhu · Zhiling Luo · Fei Li · Da Zhu

26 May 2026 at 18:00

J Cardiovasc Dev Dis. 2026 Apr 30;13(5):195. doi: 10.3390/jcdd13050195.

ABSTRACT

(1) Aim: The incidence of high-altitude pulmonary hypertension (HAPH) has risen in recent years and is expected to continue increasing; however, its diagnosis remains challenging. In this study, we employed proteomics and metabolomics to identify the proteins and metabolic biomarkers that contribute to the development of HAPH. (2) Methods: We applied integrated proteomics and metabolomics to match blood samples from 40 HAPH patients and 40 healthy controls in Yunnan's high-altitude regions to characterize molecular profiles, identify biomarkers, and develop a predictive model. (3) Results: Proteomic analysis identified four proteins (A2IPH7, K1C14, PSME2, SERPINE2) commonly dysregulated in HAPH patients from two high-altitude regions. SERPINE2 was notably downregulated and showed a negative correlation with clinical severity, which was further validated in HAPH rat lung tissues and supported by UK Biobank data for idiopathic PAH. Concurrent metabolomics uncovered 11 shared metabolites, largely acyl fatty acids, enriched in pathways such as unsaturated fatty acid synthesis. Integration of these multi-omics data enabled the development of a robust predictive model. (4) Conclusion: Our study identified key protein and metabolic biomarkers involved in HAPH development, which were validated in animal models. Based on these findings, a predictive model was developed, highlighting SERPINE2 and 11 metabolites as promising targets for the prediction and prevention of HAPH.

PMID:42188081 | DOI:10.3390/jcdd13050195

A general tensor-structured compression scheme for efficient large language models

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

By: Ying Lu · Peng-Fei Zhou · Qi-Xuan Fang · Pan Zhang · Shi-Ju Ran · Gang Su

26 May 2026 at 12:00

arXiv:2605.25344v1 Announce Type: cross Abstract: Large language models (LLMs) are dominated by dense linear transformations, whose storage, memory and computational overheads hinder efficient adaptation and deployment while masking the functional impacts of structural simplification. Here we present Tensor Mixture (MixT), a general tensor-structured compression scheme that replaces targeted dense linear layers with natively executable mixtures of tensor operators. Operating directly on generic linear projections instead of model-specific components, MixT is potentially applicable across Transformer-based LLMs and other dense neural mappings. We evaluate MixT on Qwen3-8B and LLaMA2-7B under a unified recovery protocol, identifying a broad compressible regime in which MMLU accuracy is largely preserved before an abrupt transition at model-specific boundaries. This transition coincides with coordinated shifts in output entropy, prediction entropy and inter-layer geometry. At the LLaMA2-7B transition boundary, MixT reduces full-model parameters by 47.5\%, inference FLOPs by 37.1\%, training FLOPs by 52.1\% and peak inference memory by 60.4\%, demonstrating its practical potential for lower-cost LLM compression.

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

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

By: Junli Liang · Pengfei Zhou · Wangqiu Zhou · Wenjie Qing · Qi Zhao · Ziwen Wang · Qi Song · 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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