Bowen Li

National University of Singapore

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Guanghou Shui

Institute of Genetics and Developmental Biology

Sin Man Lam

Institute of Genetics and Developmental Biology

He Tian

Zhongshan Hospital

Adam P. Harrison

Columbia VA Health Care System

Le Lü

Alibaba Group (United States)

Dar‐In Tai

Linkou Chang Gung Memorial Hospital

Simeng Qi

China University of Petroleum, Beijing

Jing Xiao

Nanjing University of Chinese Medicine

Yi Shan

National University of Singapore

Peng Ma

Chinese Academy of Medical Sciences & Peking Union Medical College

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Chinese Academy of Medical Sciences & Peking Union Medical College

Chinese Academy of Sciences

Peking University

Tsinghua University

Capital Medical University

Sun Yat-sen University

Shanghai Jiao Tong University

Northwest A&F University

University of Chinese Academy of Sciences

National University of Singapore

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Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes

Diagnostics (2023)

Adam P. Harrison Bowen Li Tse-Hwa Hsu Cheng‐Jen Chen Wanting Yu

Introduction: A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. Materials and Methods: Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3–5 images in each group were used for the results and correlated against weight changes. Results: Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026). Conclusions: The best scanning conditions are 3–5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.

Steatosis

Liver steatosis

10.3390/diagnostics13203225

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Dysregulation of acyl carnitines, pentose phosphate pathway and arginine and ornithine metabolism are associated with decline in intrinsic capacity in Chinese older adults

Aging Clinical and Experimental Research (2024)

Yiming Pan Yun Li Jagadish K. Chhetri Pan Liu Bowen Li

Abstract Background Intrinsic capacity is the combination of individual physical and mental abilities, reflecting the aging degree of the older adults. However, the mechanisms and metabolic characteristics of the decline in intrinsic capacity are still unclear. Aims To identify metabolic signatures and associated pathways of decline in intrinsic capacity based on the metabolite features. Methods We recruited 70 participants aged 77.19 ± 8.31 years. The five domains of intrinsic capacity were assessed by Short Physical Performance Battery (for mobility), Montreal cognition assessment (for cognition), 30-Item Geriatric Depression Scale (for psychology), self-reported hearing/visual impairment (for sensory) and Nutritional risk screening (for vitality), respectively. The serum samples of participants were analyzed by liquid chromatography-mass spectrometry-based metabolomics, followed by metabolite set enrichment analysis and metabolic pathway analysis. Results There were 50 participants with a decline in intrinsic capacity in at least one of the domains. A total of 349 metabolites were identified from their serum samples. Overall, 24 differential metabolites, 5 metabolite sets and 13 pathways were associated with the decline in intrinsic capacity. Discussion Our results indicated that decline in intrinsic capacity had unique metabolomic profiles. Conclusion The specific change of acyl carnitines was observed to be a feature of decline in intrinsic capacity. Dysregulation of the pentose phosphate pathway and of arginine and ornithine metabolism was strongly associated with the decline in intrinsic capacity.

Cognitive Decline

10.1007/s40520-023-02654-x

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Reliable Liver Fibrosis Assessment from Ultrasound Using Global Hetero-Image Fusion and View-Specific Parameterization

Lecture notes in computer science (2020)

Bowen Li Ke Yan Dar‐In Tai Yuankai Huo Le Lü

Overfitting

Fuse (electrical)

10.1007/978-3-030-59716-0_58

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Citations (4)

Transition State Searching Accelerated by Deep Learning Potential

Bowen Li Xiao Jin Ya Gao John Z. H. Zhang Tong Zhu

The exploration and construction of organic chemical reaction networks hold the potential to clarify complex reaction mechanisms. However, this endeavor encounters challenges, particularly in cases requiring computationally intensive quantum mechanical calculations. In recent years, the remarkable advancement of machine learning (ML) technologies has provided a novel approach to address this issue. ML can effectively utilize vast datasets to build and train complex models, enabling prediction and simulation. In this study, we conducted a performance comparison of deep potential for molecular dynamics (DeePMD), recursively embedded atom neural network (REANN), and neural equivariant interatomic potentials (NequIP) based on the Transition1x dataset. The most efficient model, NequIP, was selected for further analysis. Combined with reaction path search methods such as nudged elastic band (NEB) and growing string method (GSM), this model was employed for the identification and exploration of transition states. The results demonstrate that the success ratio of NequIP combined with the NEB method can reach 96.6%, with a mean absolute error (MAE) of 0.32 kcal/mol for barrier prediction. By adding a modest number of data points, NequIP achieves a MAE of 4.01 kcal/mol for barrier prediction in unexplored chemical reaction space. In the future, by combining different reaction network search methods, we anticipate applying this model to swiftly explore reaction pathways and construct more comprehensive reaction networks. We believe that ML will play a significant role in accelerating the elucidation of unknown chemical reaction mechanisms and in revealing complex reaction networks.

Transition state

10.26434/chemrxiv-2024-pvttd

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Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model

Sensors (2024)

Zhiwei Zhou Tao Qing Na Su J. Liu Qingzheng Chen

To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM, CNN-TL) was proposed in this study. By combining these advanced techniques, significant improvements in movement classification were achieved. Firstly, sEMG data were collected from 20 subjects as they performed four distinct gait movements: walking upstairs, walking downstairs, walking on a level surface, and squatting. Subsequently, the gathered sEMG data underwent preprocessing, with features extracted from both the time domain and frequency domain. These features were then used as inputs for the machine learning recognition model. Finally, based on the preprocessed sEMG data, the CNN-TL lower limb action recognition model was constructed. The performance of CNN-TL was then compared with that of the CNN, LSTM, and SVM models. The results demonstrated that the accuracy of the CNN-TL model in lower limb action recognition was 3.76%, 5.92%, and 14.92% higher than that of the CNN-LSTM, CNN, and SVM models, respectively, thereby proving its superior classification performance. An effective scheme for improving lower limb motor function in rehabilitation and assistance devices was thus provided.

10.3390/s24217087

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Kaempferol attenuates carbon tetrachloride (CCl4)-induced hepatic fibrosis by promoting ASIC1a degradation and suppression of the ASIC1a-mediated ERS

Phytomedicine (2023)

Rui Cao Chun Cao Xiaojie Hu Kang Du Jingrong Zhang

Hepatic stellate cell

Hepatic fibrosis

10.1016/j.phymed.2023.155125

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Citations (21)

Lysophospholipids and branched chain amino acids are associated with aging: a metabolomics-based study of Chinese adults

European journal of medical research (2023)

Yiming Pan Pan Liu Shijie Li Bowen Li Yun Li

Abstract Background Aging is an inevitable process associated with impairments in multiple organ systems, which increases the risk of comorbidity and disability, and reduces the health-span. Metabolomics is a powerful tool in aging research, which can reflect the characteristics of aging at the level of terminal metabolism, and may contribute to the exploration of aging mechanisms and the formulation of anti-aging strategies. Methods To identify possible biomarkers and pathways associated with aging using untargeted metabolomics methods, we performed liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics profiling on serum samples from 32 older adults and 32 sex-matched young controls. Results Metabolite profiling could distinguish the two groups. Among the 349 metabolites identified, 80—including lysophospholipids whose levels gradually decline—are possible candidate aging biomarkers. Valine, leucine and isoleucine degradation and biosynthesis were important pathways in aging, with reduced levels of l -isoleucine ( r = − 0.30, p = 0.017) and l -leucine ( r = − 0.32, p = 0.010) observed in older adults. Conclusions We preliminarily revealed the metabolite changes associated with aging in Chinese adults. Decreases in mitochondrial membrane-related lysophospholipids and dysfunction of branched-chain amino acid metabolism were determined to be the characteristics and promising research targets for aging.

Isoleucine

Metabolome

Catabolism

10.1186/s40001-023-01021-w

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Localized increases in CEPT1 and ATGL elevate plasmalogen phosphatidylcholines in HDLs contributing to atheroprotective lipid profiles in hyperglycemic GCK-MODY

Redox Biology (2021)

Xiaojing Wang Sin Man Lam Mingjun Cao Tong Wang Zhixin Wang

Glucokinase-maturity onset diabetes of the young (GCK-MODY) represents a rare genetic disorder due to mutation in the glucokinase (GCK) gene. The low incidence of vascular complications in GCK-MODY makes it a natural paradigm for interrogating molecular mechanisms promoting vascular health under prolonged hyperglycemia. Clinical rate of misdiagnosis has remained high, and a reliable serum lipid biomarker that precedes genetic screening can facilitate correct diagnosis and treatment. Herein, we comprehensively quantitated 565 serum lipids from 25 classes in 105 subjects (42 nondiabetic controls, 30 GC K-MODY patients, 33 drug-naïve, and newly-onset T2D patients). At false-discovery rate (FDR) < 0.05, several phosphatidylcholines (PCs) and plasmalogen PCs were specifically increased in GCK-MODY, while triacylglycerols (TAGs) and diacylglycerols (DAGs) were reduced. Correlation matrices between lipids uncovered coregulation between plasmalogen PCs (PCps) and glycerolipid precursors was distinctly enhanced in GCK-MODY compared to T2D. Strengthened positive correlations between serum PCps and circulating HDLs was specifically observed in hyperglycemic subjects (i.e. T2D and GCK-MODY) compared to normglycemic controls, suggesting that HDL-PCps may elicit distinct physiological effects under hyperglycemia. Amongst GCK-MODY patients, individuals harboring variants of GCK mutations with elevated PCps also exhibited higher HDLs. Isolated HDLs displayed localized increases (p < 0.05) in very-long-chain PUFA-PCs and PCps in GCK-MODY. Protein analyses revealed elevated levels of HDL-resident ATGL (P = 0.003) and CEPT1 (P < 0.0001), which mediate critical steps of PCps production along the TAG-DAG-PC axis, in GCK-MODY relative to T2D. A panel of four lipids differentiated GCK-MODY from T2D with AUC of 0.950 (95% CI 0.903–9.997). This study provides the first evidence that enhanced recruitment of CEPT1 and ATGL onto HDLs essentially underlie the atheroprotective profiles associated with GCK-MODY. Resultant increases in the production of HDL-PCps and PUFA-PCs provides an active, circulating form of protection towards the vasculature of GCK-MODY, thereby lowering the incidence of vascular complications despite chronic exposure to hyperglycemia since birth.

Glucokinase

Plasmalogen

10.1016/j.redox.2021.101855

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Citations (17)

Systematic Discovery and Pathway Analyses of Metabolic Disturbance in COVID-19

Infectious Diseases & Immunity (2021)

Bowen Li Xing Fan Wen-Jing Cao He Tian Siyu Wang

Abstract Background: The ongoing global coronavirus disease 2019 (COVID-19) pandemic is posing a serious public health threat to nations worldwide. Understanding the pathogenesis of the disease and host immune responses will facilitate the discovery of therapeutic targets and better management of infected patients. Metabolomics technology can provide an unbiased tool to explore metabolic perturbation. Methods: Twenty-six healthy controls and 50 COVID-19 patients with mild, moderate, and severe symptoms in the Fifth Medical Center of PLA General Hospital from January 22 to February 16, 2020 were recruited into the study. Fasting blood samples were collected and subject to metabolomics analysis by liquid chromatography–mass spectrometry. Metabolite abundance was measured by peak area and was log-transformed before statistical analysis. The principal component analysis, different expression analysis, and metabolic pathway analysis were performed using R package. Co-regulated metabolites and their associations with clinical indices were identified by the weighted correlation network analysis and Spearman correlation coefficients. The potential metabolite biomarkers were analyzed using a random forest model. Results: We uncovered over 100 metabolites that were associated with COVID-19 disease and many of them correlated with disease severity. Sets of highly correlated metabolites were identified and their correlations with clinical indices were presented. Further analyses linked the differential metabolites with biochemical reactions, metabolic pathways, and biomedical MeSH terms, offering contextual insights into disease pathogenesis and host responses. Finally, a panel of metabolites was discovered to be able to discriminate COVID-19 patients from healthy controls, and also another list for mild against more severe cases. Our findings showed that in COVID-19 patients, citrate cycle, sphingosine 1-phosphate in sphingolipid metabolism, and steroid hormone biosynthesis were downregulated, while purine metabolism and tryptophan metabolism were disturbed. Conclusion: This study discovered key metabolites as well as their related biological and medical concepts pertaining to COVID-19 pathogenesis and host immune response, which will facilitate the selection of potential biomarkers for prognosis and discovery of therapeutic targets.

Pathogenesis

Pandemic

Metabolic pathway

10.1097/id9.0000000000000010

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Integrative analyses of bulk and single-cell RNA-seq reveals the correlation between SPP1+ macrophages and resistance to neoadjuvant chemoimmunotherapy in esophageal squamous cell carcinoma

Cancer Immunology Immunotherapy (2024)

Zhen-Yang Geng Feng Li Zhichang Yang Bowen Li Yifan Xu

Neoadjuvant chemoimmunotherapy (NACI) has significant implications for the treatment of esophageal cancer. However, its clinical efficacy varies considerably among patients, necessitating further investigation into the underlying mechanisms. The rapid advancement of single-cell RNA sequencing (scRNA-seq) technology facilitates the analysis of patient heterogeneity at the cellular level, particularly regarding treatment outcomes. In this study, we first analyzed scRNA-seq data of esophageal squamous cell carcinoma (ESCC) following NACI, obtained from the Gene Expression Omnibus (GEO) database. After performing dimensionality reduction, clustering, and annotation on the scRNA-seq data, we employed CellChat to investigate differences in cell–cell communication among samples from distinct efficacy groups. The results indicated that macrophages in the non-responder exhibited stronger cell communication intensity compared to those in responders, with SPP1 and GALECTIN signals showing the most significant differences between the two groups. This finding underscores the crucial role of macrophages in the efficacy of NACI. Subsequently, reclustering of macrophages revealed that Mac-SPP1 may be primarily responsible for treatment resistance, while Mac-C1QC appears to promote T cell activation. Finally, we conducted transcriptome sequencing on ESCC tissues obtained from 32 patients who underwent surgery following NACI. Utilizing CIBERSORT, CIBERSORTx, and WGCNA, we analyzed the heterogeneity of tumor microenvironment among different efficacy groups and validated the correlation between SPP1+ macrophages and resistance to NACI in ESCC using publicly available transcriptome sequencing datasets. These findings suggest that SPP1+ macrophages may represent a key factor contributing to resistance against NACI in ESCC.

Chemoimmunotherapy

Neoadjuvant Therapy

RNA-Seq

10.1007/s00262-024-03848-6

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