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Article ; Online: Multi-omics reveals changed energy metabolism of liver and muscle by caffeine after mice swimming.

Han, Yang / Jia, Qian / Tian, Yu / Yan, Yan / He, Kunlun / Zhao, Xiaojing

2024 Volume 12, Page(s) e16677

Abstract: In recent years, numerous studies have investigated the effects of caffeine on exercise, and provide convincing evidence for its ergogenic effects on exercise performance. However, the precise mechanisms underlying these ergogenic effects remain unclear. ...

Abstract	In recent years, numerous studies have investigated the effects of caffeine on exercise, and provide convincing evidence for its ergogenic effects on exercise performance. However, the precise mechanisms underlying these ergogenic effects remain unclear. In this study, an exercise swimming model was conducted to investigate the effects of orally administered with caffeine before swimming on the alterations of proteome and energy metabolome of liver and muscle after swimming. We found proteins in liver, such as S100a8, S100a9, Gabpa, Igfbp1 and Sdc4, were significantly up-regulated, while Rbp4 and Tf decreased after swimming were further down-regulated in caffeine group. The glycolysis and pentose phosphate pathways in liver and muscle were both significantly down-regulated in caffeine group. The pyruvate carboxylase and amino acid levels in liver, including cysteine, serine and tyrosine, were markedly up-regulated in caffeine group, exhibiting a strong correlation with the increased pyruvic acid and oxaloacetate levels in muscle. Moreover, caffeine significantly decreased the lactate levels in both liver and muscle after swimming, potentially benefiting exercise performance.
MeSH term(s)	Animals ; Mice ; Caffeine/pharmacology ; Swimming ; Multiomics ; Performance-Enhancing Substances ; Liver ; Muscles ; Energy Metabolism
Chemical Substances	Caffeine (3G6A5W338E) ; Performance-Enhancing Substances
Language	English
Publishing date	2024-01-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	2703241-3
ISSN	2167-8359 ; 2167-8359
ISSN (online)	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.16677
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SGT++: Improved Scene Graph-Guided Transformer for Surgical Report Generation.

Lin, Chen / Zhu, Zhenfeng / Zhao, Yawei / Zhang, Ying / He, Kunlun / Zhao, Yao

IEEE transactions on medical imaging

2024 Volume 43, Issue 4, Page(s) 1337–1346

Abstract: Automatically recording surgical procedures and generating surgical reports are crucial for alleviating surgeons' workload and enabling them to concentrate more on the operations. Despite some achievements, there still exist several issues for the ... ...

Abstract	Automatically recording surgical procedures and generating surgical reports are crucial for alleviating surgeons' workload and enabling them to concentrate more on the operations. Despite some achievements, there still exist several issues for the previous works: 1) failure to model the interactive relationship between surgical instruments and tissue; and 2) neglect of fine-grained differences within different surgical images in the same surgery. To address these two issues, we propose an improved scene graph-guided Transformer, also named by SGT++, to generate more accurate surgical report, in which the complex interactions between surgical instruments and tissue are learnt from both explicit and implicit perspectives. Specifically, to facilitate the understanding of the surgical scene graph under a graph learning framework, a simple yet effective approach is proposed for homogenizing the input heterogeneous scene graph. For the homogeneous scene graph that contains explicit structured and fine-grained semantic relationships, we design an attention-induced graph transformer for node aggregation via an explicit relation-aware encoder. In addition, to characterize the implicit relationships about the instrument, tissue, and the interaction between them, the implicit relational attention is proposed to take full advantage of the prior knowledge from the interactional prototype memory. With the learnt explicit and implicit relation-aware representations, they are then coalesced to obtain the fused relation-aware representations contributing to generating reports. Some comprehensive experiments on two surgical datasets show that the proposed STG++ model achieves state-of-the-art results.
MeSH term(s)	Humans ; Semantics ; Surgeons
Language	English
Publishing date	2024-04-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2023.3335909
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Article ; Online: Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data.

Hu, Dayu / Liang, Ke / Dong, Zhibin / Wang, Jun / Zhao, Yawei / He, Kunlun

Briefings in bioinformatics

2024 Volume 25, Issue 2

Abstract: In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data ... ...

Abstract	In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.
MeSH term(s)	Chromatin ; Single-Cell Gene Expression Analysis ; Cluster Analysis ; Learning ; RNA, Small Cytoplasmic/genetics ; Transposases ; Sequence Analysis, RNA ; Gene Expression Profiling
Chemical Substances	Chromatin ; RNA, Small Cytoplasmic ; Transposases (EC 2.7.7.-)
Language	English
Publishing date	2024-03-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbae102
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Article ; Online: On the Consistency and Large-Scale Extension of Multiple Kernel Clustering.

Liang, Weixuan / Tang, Chang / Liu, Xinwang / Liu, Yong / Liu, Jiyuan / Zhu, En / He, Kunlun

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. ... ...

Abstract	Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets. This paper tries to address the above two issues. We first make a consistency analysis of an influential MKC method named Simple Multiple Kernel k-Means (SimpleMKKM). Specifically, suppose that ∧γ
Language	English
Publishing date	2024-04-11
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3387433
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Article ; Online: Serum uric acid and outcome in hospitalized elderly patients with chronic heart failure through the whole spectrum of ejection fraction phenotypes.

Yan, Wei / Tang, Hai-Ying / Yang, Yong-Qiang / He, Kun-Lun

BMC cardiovascular disorders

2023 Volume 23, Issue 1, Page(s) 589

Abstract: Introduction: Elevated serum uric acid (SUA) levels have been associated with poor outcome in patients with heart failure (HF). Uric acid is associated with inflammation and microvascular dysfunction, which may differentially affect left ventricular ... ...

Abstract	Introduction: Elevated serum uric acid (SUA) levels have been associated with poor outcome in patients with heart failure (HF). Uric acid is associated with inflammation and microvascular dysfunction, which may differentially affect left ventricular ejection fraction (EF) phenotypes. We aimed to identify the role of SUA across EF phenotypes in hospitalized elderly patients with chronic HF. Methods: We analyzed 1355 elderly patients who were diagnosed with chronic HF. All patients had SUA levels measured within the first 24 h following admission. Patients with left ventricle EF were categorized as having HF with reduced EF (HFrEF, EF < 40%), HF with mid-range EF (HFmrEF, 40%≦LVEF ≦ 49%) or HF with preserved EF (HFpEF, LVEF ≥ 50%). Endpoints were cardiovascular death, HF rehospitalization, and their composite. The median follow-up period was 18 months. Results: Compared with the lowest SUA quartile, the highest SUA quartile was significantly associated with the endpoints (adjusted HR: 2.404, 95% CI: 1.178-4.906, P = 0.016; HR: 1.418, 95% CI: 1.021-1.971, P = 0.037; HR: 1.439, 95% CI: 1.049-1.972, P = 0.024, respectively). After model adjustment, a significant association of SUA with cardiovascular death and the composite endpoint persisted among HFrEF and HFmrEF patients in the highest SUA quartile (P < 0.05 for all). Conclusions: In hospitalized elderly patients with chronic HF, SUA is an independent predictor of adverse outcomes, which can be seen in HFrEF and HFmrEF patients.
MeSH term(s)	Humans ; Aged ; Heart Failure/diagnosis ; Stroke Volume ; Ventricular Function, Left ; Uric Acid ; Prognosis ; Chronic Disease
Chemical Substances	Uric Acid (268B43MJ25)
Language	English
Publishing date	2023-11-30
Publishing country	England
Document type	Journal Article
ZDB-ID	2059859-2
ISSN	1471-2261 ; 1471-2261
ISSN (online)	1471-2261
ISSN	1471-2261
DOI	10.1186/s12872-023-03544-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Medical Federated Model with Mixture of Personalized and Sharing Components

Zhao, Yawei / Liu, Qinghe / Liu, Xinwang / He, Kunlun

2023

Abstract: Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable ... ...

Abstract	Although data-driven methods usually have noticeable performance on disease diagnosis and treatment, they are suspected of leakage of privacy due to collecting data for model training. Recently, federated learning provides a secure and trustable alternative to collaboratively train model without any exchange of medical data among multiple institutes. Therefore, it has draw much attention due to its natural merit on privacy protection. However, when heterogenous medical data exists between different hospitals, federated learning usually has to face with degradation of performance. In the paper, we propose a new personalized framework of federated learning to handle the problem. It successfully yields personalized models based on awareness of similarity between local data, and achieves better tradeoff between generalization and personalization than existing methods. After that, we further design a differentially sparse regularizer to improve communication efficiency during procedure of model training. Additionally, we propose an effective method to reduce the computational cost, which improves computation efficiency significantly. Furthermore, we collect 5 real medical datasets, including 2 public medical image datasets and 3 private multi-center clinical diagnosis datasets, and evaluate its performance by conducting nodule classification, tumor segmentation, and clinical risk prediction tasks. Comparing with 13 existing related methods, the proposed method successfully achieves the best model performance, and meanwhile up to 60% improvement of communication efficiency. Source code is public, and can be accessed at: https://github.com/ApplicationTechnologyOfMedicalBigData/pFedNet-code. Comment: Medical data, federated learning, personalized model, similarity network
Keywords	Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-06-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Impact of body mass index on long-term outcomes in patients undergoing percutaneous coronary intervention stratified by diabetes mellitus: a retrospective cohort study.

Rao, Chongyou / Zhong, Qin / Wu, Rilige / Li, Zongren / Duan, Yongjie / Zhou, You / Wang, Chi / Chen, Xu / Wang, Ruiqing / He, Kunlun

BMC cardiovascular disorders

2024 Volume 24, Issue 1, Page(s) 113

Abstract: Background: Patients with diabetes mellitus (DM) caused by obesity have increased in recent years. The impact of obesity on long-term outcomes in patients undergoing percutaneous coronary intervention (PCI) with or without DM remains unclear.: Methods! ...

Abstract	Background: Patients with diabetes mellitus (DM) caused by obesity have increased in recent years. The impact of obesity on long-term outcomes in patients undergoing percutaneous coronary intervention (PCI) with or without DM remains unclear. Methods: We retrospectively analysed data from 1918 patients who underwent PCI. Patients were categorized into four groups based on body mass index (BMI, normal weight: BMI < 25 kg/m Results: During a median follow-up of 7.0 years, no significant differences in MACCE, myocardial infarction, or stroke were observed among the four groups. Overweight and obese individuals exhibited lower all-cause mortality rates compared with normal-weight patients (without DM: hazard ratio [HR]: 0.54, 95% confidence interval [CI]: 0.37 to 0.78; with DM: HR: 0.57, 95% CI: 0.38 to 0.86). In non-diabetic patients, the overweight and obese group demonstrated a higher risk of unplanned repeat revascularization than the normal-weight group (HR:1.23, 95% CI:1.03 to 1.46). After multivariable adjustment, overweight and obesity were not significantly associated with MACCE, all-cause death, myocardial infarction, stroke, or unplanned repeat revascularization in patients with and without diabetes undergoing PCI. Conclusion: Overweight and obesity did not demonstrate a significant protective effect on long-term outcomes in patients with and without diabetes undergoing PCI.
MeSH term(s)	Humans ; Overweight ; Retrospective Studies ; Body Mass Index ; Percutaneous Coronary Intervention/adverse effects ; Diabetes Mellitus/diagnosis ; Diabetes Mellitus/epidemiology ; Myocardial Infarction/etiology ; Obesity/complications ; Obesity/diagnosis ; Stroke/diagnosis ; Stroke/epidemiology ; Stroke/complications ; Treatment Outcome ; Coronary Artery Disease/diagnostic imaging ; Coronary Artery Disease/therapy ; Coronary Artery Disease/complications
Language	English
Publishing date	2024-02-16
Publishing country	England
Document type	Journal Article
ZDB-ID	2059859-2
ISSN	1471-2261 ; 1471-2261
ISSN (online)	1471-2261
ISSN	1471-2261
DOI	10.1186/s12872-024-03770-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: HyperTMO: a trusted multi-omics integration framework based on hypergraph convolutional network for patient classification.

Wang, Haohua / Lin, Kai / Zhang, Qiang / Shi, Jinlong / Song, Xinyu / Wu, Jue / Zhao, Chenghui / He, Kunlun

Bioinformatics (Oxford, England)

2024 Volume 40, Issue 4

Abstract: Motivation: The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to ... ...

Abstract	Motivation: The rapid development of high-throughput biomedical technologies can provide researchers with detailed multi-omics data. The multi-omics integrated analysis approach based on machine learning contributes a more comprehensive perspective to human disease research. However, there are still significant challenges in representing single-omics data and integrating multi-omics information. Results: This article presents HyperTMO, a Trusted Multi-Omics integration framework based on Hypergraph convolutional network for patient classification. HyperTMO constructs hypergraph structures to represent the association between samples in single-omics data, then evidence extraction is performed by hypergraph convolutional network, and multi-omics information is integrated at an evidence level. Last, we experimentally demonstrate that HyperTMO outperforms other state-of-the-art methods in breast cancer subtype classification and Alzheimer's disease classification tasks using multi-omics data from TCGA (BRCA) and ROSMAP datasets. Importantly, HyperTMO is the first attempt to integrate hypergraph structure, evidence theory, and multi-omics integration for patient classification. Its accurate and robust properties bring great potential for applications in clinical diagnosis. Availability and implementation: HyperTMO and datasets are publicly available at https://github.com/ippousyuga/HyperTMO.
MeSH term(s)	Humans ; Female ; Multiomics ; Alzheimer Disease ; Breast ; Breast Neoplasms/genetics ; Machine Learning
Language	English
Publishing date	2024-03-26
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btae159
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Zs.A 2374: Show issues

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Article ; Online: Longitudinal multi-omics analysis uncovers the altered landscape of gut microbiota and plasma metabolome in response to high altitude.

Han, Yang / Liu, Xiaoshuang / Jia, Qian / Xu, Jiayu / Shi, Jinlong / Li, Xiang / Xie, Guotong / Zhao, Xiaojing / He, Kunlun

Microbiome

2024 Volume 12, Issue 1, Page(s) 70

Abstract: Background: Gut microbiota is significantly influenced by altitude. However, the dynamics of gut microbiota in relation to altitude remains undisclosed.: Methods: In this study, we investigated the microbiome profile of 610 healthy young men from ... ...

Abstract	Background: Gut microbiota is significantly influenced by altitude. However, the dynamics of gut microbiota in relation to altitude remains undisclosed. Methods: In this study, we investigated the microbiome profile of 610 healthy young men from three different places in China, grouped by altitude, duration of residence, and ethnicity. We conducted widely targeted metabolomic profiling and clinical testing to explore metabolic characteristics. Results: Our findings revealed that as the Han individuals migrated from low altitude to high latitude, the gut microbiota gradually converged towards that of the Tibetan populations but reversed upon returning to lower altitude. Across different cohorts, we identified 51 species specifically enriched during acclimatization and 57 species enriched during deacclimatization to high altitude. Notably, Prevotella copri was found to be the most enriched taxon in both Tibetan and Han populations after ascending to high altitude. Furthermore, significant variations in host plasma metabolome and clinical indices at high altitude could be largely explained by changes in gut microbiota composition. Similar to Tibetans, 41 plasma metabolites, such as lactic acid, sphingosine-1-phosphate, taurine, and inositol, were significantly elevated in Han populations after ascending to high altitude. Germ-free animal experiments demonstrated that certain species, such as Escherichia coli and Klebsiella pneumoniae, which exhibited altitude-dependent variations in human populations, might play crucial roles in host purine metabolism. Conclusions: This study provides insights into the dynamics of gut microbiota and host plasma metabolome with respect to altitude changes, indicating that their dynamics may have implications for host health at high altitude and contribute to host adaptation. Video Abstract.
MeSH term(s)	Animals ; Male ; Humans ; Gastrointestinal Microbiome/genetics ; Altitude ; Multiomics ; Metabolome ; East Asian People
Language	English
Publishing date	2024-04-05
Publishing country	England
Document type	Video-Audio Media ; Journal Article
ZDB-ID	2697425-3
ISSN	2049-2618 ; 2049-2618
ISSN (online)	2049-2618
ISSN	2049-2618
DOI	10.1186/s40168-024-01781-5
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Article ; Online: Methylation of NRIP3 Is a Synthetic Lethal Marker for Combined PI3K and ATR/ATM Inhibitors in Colorectal Cancer.

Zhang, Meiying / Li, Xiaoyun / Herman, James G / Gao, Aiai / Wang, Qian / Yao, Yuanxin / Shen, Fangfang / He, Kunlun / Guo, Mingzhou

Clinical and translational gastroenterology

2024 Volume 15, Issue 3, Page(s) e00682

Abstract: Introduction: The aim of this study was to investigate the epigenetic regulation and underlying mechanism of NRIP3 in colorectal cancer (CRC).: Methods: Eight cell lines (SW480, SW620, DKO, LOVO, HT29, HCT116, DLD1, and RKO), 187 resected margin ... ...

Abstract	Introduction: The aim of this study was to investigate the epigenetic regulation and underlying mechanism of NRIP3 in colorectal cancer (CRC). Methods: Eight cell lines (SW480, SW620, DKO, LOVO, HT29, HCT116, DLD1, and RKO), 187 resected margin samples from colorectal cancer tissue, 146 cases with colorectal adenomatous polyps, and 308 colorectal cancer samples were used. Methylation-specific PCR, Western blotting, RNA interference assay, and a xenograft mouse model were used. Results: NRIP3 exhibited methylation in 2.7% (5/187) of resected margin samples from colorectal cancer tissue, 32.2% (47/146) of colorectal adenomatous polyps, and 50.6% (156/308) of CRC samples, and the expression of NRIP3 was regulated by promoter region methylation. The methylation of NRIP3 was found to be significantly associated with late onset (at age 50 years or older), poor tumor differentiation, lymph node metastasis, and poor 5-year overall survival in CRC (all P < 0.05). In addition, NRIP3 methylation was an independent poor prognostic marker ( P < 0.05). NRIP3 inhibited cell proliferation, colony formation, invasion, and migration, while induced G1/S arrest. NRIP3 suppressed CRC growth by inhibiting PI3K-AKT signaling both in vitro and in vivo . Methylation of NRIP3 sensitized CRC cells to combined PI3K and ATR/ATM inhibitors. Discussion: NRIP3 was frequently methylated in both colorectal adenomatous polyps and CRC. The methylation of NRIP3 may potentially serve as an early detection, late-onset, and poor prognostic marker in CRC. NRIP3 is a potential tumor suppressor. NRIP3 methylation is a potential synthetic lethal marker for combined PI3K and ATR/ATM inhibitors.
MeSH term(s)	Humans ; Animals ; Mice ; Middle Aged ; DNA Methylation ; Epigenesis, Genetic ; Cell Line, Tumor ; Phosphatidylinositol 3-Kinases/genetics ; Phosphatidylinositol 3-Kinases/metabolism ; HCT116 Cells ; Colorectal Neoplasms/drug therapy ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/metabolism ; Adenomatous Polyps/genetics ; Ataxia Telangiectasia Mutated Proteins/genetics ; Ataxia Telangiectasia Mutated Proteins/metabolism
Chemical Substances	Phosphatidylinositol 3-Kinases (EC 2.7.1.-) ; ATM protein, human (EC 2.7.11.1) ; Ataxia Telangiectasia Mutated Proteins (EC 2.7.11.1) ; ATR protein, human (EC 2.7.11.1)
Language	English
Publishing date	2024-03-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2581516-7
ISSN	2155-384X ; 2155-384X
ISSN (online)	2155-384X
ISSN	2155-384X
DOI	10.14309/ctg.0000000000000682
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