Bairong Shen’s research while affiliated with Sichuan University and other places

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Publications (68)


Evolutionarily new genes in humans with disease phenotypes reveal functional enrichment patterns shaped by adaptive innovation and sexual selection
  • Article

February 2025

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4 Reads

Genome Research

Jian-Hai Chen

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Manyuan Long

New genes (or young genes) are genetic novelties pivotal in mammalian evolution. However, their phenotypic impacts and evolutionary patterns over time remain elusive in humans due to the technical and ethical complexities of functional studies. Integrating gene age dating with Mendelian disease phenotyping, we reveal a gradual rise in disease gene proportion as gene age increases. Logistic regression modeling indicates that this increase in older genes may be related to their longer sequence lengths and higher burdens of deleterious de novo germline variants (DNVs). We also find a steady integration of new genes with biomedical phenotypes into the human genome over macroevolutionary timescales (~0.07% per million years). Despite this stable pace, we observe distinct patterns in phenotypic enrichment, pleiotropy, and selective pressures across gene ages. Young genes show significant enrichment in diseases related to the male reproductive system, indicating strong sexual selection. Young genes also exhibit disease-related functions potentially linked to human phenotypic innovations, such as increased brain size, musculoskeletal phenotypes, and color vision. We further reveal a logistic growth pattern of pleiotropy over evolutionary time, indicating a diminishing marginal growth of new functions for older genes due to intensifying selective constraints over time. We propose a "pleiotropy-barrier" model that delineates higher potentials for phenotypic in-novation in young genes compared to older genes, a process under natural selection. Our study demonstrates that evolutionarily new genes are critical in influencing human reproductive evolution and adaptive phenotypic innovations driven by sexual and natural selection, with low pleiotropy as a selective advantage.


Translational Informatics Driven Drug Repositioning for Neurodegenerative Disease

February 2025

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2 Reads

Current Neuropharmacology

Neurodegenerative diseases represent a prevalent category of age-associated diseases. As human lifespans extend and societies become increasingly aged, neurodegenerative diseases pose a growing threat to public health. The lack of effective therapeutic drugs for both common and rare neurodegenerative diseases amplifies the medical challenges they present. Current treatments for these diseases primarily offer symptomatic relief rather than a cure, underscoring the pressing need to develop efficacious therapeutic interventions. Drug repositioning, an innovative and data-driven approach to research and development, proposes the re-evaluation of existing drugs for potential application in new therapeutic areas. Fueled by rapid advancements in artificial intelligence and the burgeoning accumulation of medical data, drug repositioning has emerged as a promising pathway for drug discovery. This review comprehensively examines drug repositioning for neurodegenerative diseases through the lens of translational informatics, encompassing data sources, computational models, and clinical applications. Initially, we systematized drug repositioning-related databases and online platforms, focusing on data resource management and standardization. Subsequently, we classify computational models for drug repositioning from the perspectives of drug-drug, drug-target, and drug-disease interactions into categories such as machine learning, deep learning, and networkbased approaches. Lastly, we highlight computational models presently utilized in neurodegenerative disease research and identify databases that hold potential for future drug repositioning efforts. In the artificial intelligence era, drug repositioning, as a data-driven strategy, offers a promising avenue for developing treatments suited to the complex and multifaceted nature of neurodegenerative diseases. These advancements could furnish patients with more rapid, cost-effective therapeutic options.


Data pipeline diagram for standardized data collection from EMR
Diagram illustrating the development of ML models for initializing the TIA screening ML-LHS unit. This unit is designed to enhance inclusive risk-based TIA screening in both urban and rural populations. Step 1: Building an inclusive model from hospital EMR data using a data-centric approach. Step 2: Refining this model into a more practical model by applying a quantitative distribution of TIA risk factors. Step 3: Initializing the ML-LHS unit by internally validating the practical model using new EMR data. Step 4: Externally validating the practical model with data from different hospitals. Post-initialization, the unit is prepared to provide risk prediction for patients in the clinical research network (CRN). After deployment for routine clinical screening, at-risk patients will be identified for early detection. Continuous learning cycles, fed by new data from the service, will facilitate ongoing building and validation of models within the embedded research. Pragmatic clinical trials (PCT) will generate robust evidence for the rapid evolution of the ML-LHS unit. TIA: transient ischemic attack. ML: machine learning. LHS: learning health system
Trend analysis of TIA risk prediction performance in XGBoost models with varying numbers of variables. The XGBoost base models were developed using default settings. TIA: transient ischemic attack
Performance and reliability curves of XGBoost models for TIA risk prediction. The base models used default settings. The inclusive 150-variable model: ROC curve (a) and reliability curve (b). The practical 20-variable model: ROC curve (c) and reliability curve (d). The first internally validated updated model in the TIA ML-LHS unit: ROC curve (e) and reliability curve (f). TIA: transient ischemic attack. ML: machine learning. LHS: learning health system
Baseline characteristics of TIA patients and background patients

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Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records
  • Article
  • Full-text available

December 2024

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3 Reads

BMC Medical Informatics and Decision Making

Background Patients with transient ischemic attack (TIA) face a significantly increased risk of stroke. However, TIA screening and early detection rates are low, especially in developing countries. This study aims to develop an inclusive and practical TIA risk prediction model using machine learning (ML) that performs well in both hospital and resource-limited clinic settings. This model is essential for initiating the first ML-enabled learning health system (LHS) unit designed for routine and equitable TIA screening and early detection across broad populations. Methods Employing a novel protocol, this study first standardized data from a hospital’s electronic medical records (EMR) to construct inclusive TIA risk prediction ML models using a data-centric approach. Subsequently, a quantitative distribution of TIA risk factors was applied in feature engineering to reduce the number of variables for a practical ML model. This refined model initiated a TIA ML-LHS unit that is capable of continuously updating with new EMR data from hospitals and clinics. Additionally, the practical model underwent external validation using data from another hospital. Results The inclusive 150-variable ML models, derived from all available EMR variables for TIA, achieved a recall of 0.868 and an accuracy of 0.886 in predicting TIA risk. Further feature engineering produced a practical XGBoost model with 20 variables, maintaining acceptable performance of 0.855 recall and 0.796 accuracy. The initialized TIA ML-LHS unit, based on the practical model, achieved performance metrics of 0.830 recall, 0.726 precision, 0.816 ROC-AUC, and 0.812 accuracy. The model also performed well in external validation, confirming its effectiveness with patient data from different clinical settings. Conclusions This study developed the first inclusive and practical TIA XGBoost model from full hospital EHR and initiated the first TIA risk prediction ML-LHS unit. This TIA model, which requires only 20 variables, enables the ML-LHS to serve not only patients in hospitals but also those in resource-limited clinics. These results have significant implications for expanding risk-based TIA screening in community and rural clinics, thereby enhancing early detection of TIA among underserved populations and improving health equity. The novel protocol used in this study is also applicable for initiating ML-LHS units for various preventable diseases, providing a new system-level approach to responsible AI development and applications.

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Bibliometric and visual analysis of human microbiome—breast cancer interactions: current insights and future directions

The composition of the gut microbiome differs from that of healthy individuals and is closely linked to the progression and development of breast cancer. Recent studies have increasingly examined the relationship between microbial communities and breast cancer. This study analyzed the research landscape of microbiome and breast cancer, focusing on 736 qualified publications from the Web of Science Core Collection (WoSCC). Publications in this field are on the rise, with the United States leading in contributions, followed by China and Italy. Despite this strong output, the centrality value of China in this field is comparatively low at ninth, highlighting a gap between the quantity of research and its global impact. This pattern is repetitively observed in institutional contributions, with a predominance of Western institutes among the top contributors, underscoring a potential research quality gap in China. Keyword analysis reveals that research hotspots are focused on the effect of microbiome on breast cancer pathogenesis and tumor metabolism, with risk factors and metabolic pathways being the most interesting areas. Publications point to a shift toward anti-tumor therapies and personalized medicine, with clusters such as “anti-tumor” and “potential regulatory agent” gaining prominence. Additionally, intratumor bacteria studies have emerged as a new area of significant interest, reflecting a new direction in research. The University of Helsinki and Adlercreutz H are influential institutions and researchers in this field. Current trends in microbiome and breast cancer research indicate a significant shift toward therapeutic applications and personalized medicine. Strengthening international collaborations and focusing on research quality is crucial for advancing microbiome and breast cancer research.




Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study

September 2024

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8 Reads

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1 Citation

JMIR AI

Background A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening. Objective This study aims to realize the LHS vision by designing an equitable, machine learning (ML)–enabled LHS unit for LC screening. It focuses on developing an inclusive and practical LC risk prediction model, suitable for initializing the ML-enabled LHS (ML-LHS) unit. This model aims to empower primary physicians in a clinical research network, linking central hospitals and rural clinics, to routinely deliver risk-based screening for enhancing LC early detection in broader populations. Methods We created a standardized data set of health factors from 1397 patients with LC and 1448 control patients, all aged 30 years and older, including both smokers and nonsmokers, from a hospital’s electronic medical record system. Initially, a data-centric ML approach was used to create inclusive ML models for risk prediction from all available health factors. Subsequently, a quantitative distribution of LC health factors was used in feature engineering to refine the models into a more practical model with fewer variables. Results The initial inclusive 250-variable XGBoost model for LC risk prediction achieved performance metrics of 0.86 recall, 0.90 precision, and 0.89 accuracy. Post feature refinement, a practical 29-variable XGBoost model was developed, displaying performance metrics of 0.80 recall, 0.82 precision, and 0.82 accuracy. This model met the criteria for initializing the ML-LHS unit for risk-based, inclusive LC screening within clinical research networks. Conclusions This study designed an innovative ML-LHS unit for a clinical research network, aiming to sustainably provide inclusive LC screening to all at-risk populations. It developed an inclusive and practical XGBoost model from hospital electronic medical record data, capable of initializing such an ML-LHS unit for community and rural clinics. The anticipated deployment of this ML-LHS unit is expected to significantly improve LC-screening rates and early detection among broader populations, including those typically overlooked by existing screening guidelines.


Mitochondrial Dysfunction in Epilepsy. ROS: Reactive oxygen species; ATP: Adenosine triphosphate.
Imbalances in mitochondrial fusion and fission dynamics.
Role of calcium in excitotoxicity. TCA: Tricarboxylic acid.
Calcium imbalance and Neuronal Excitability caused by mitochondrial disfunction. NMDA: N-Methyl D- Aspartate, ROS: Reactive Oxygen Species, ATP: Adenosine triphosphate, Na⁺/K⁺ ATPase: Sodium Potassium ATPase pump.
Therapeutic strategies targeting mitochondria in epilepsy.
Unraveling the nexus of age, epilepsy, and mitochondria: exploring the dynamics of cellular energy and excitability

September 2024

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153 Reads

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3 Citations

Epilepsy, a complex neurological condition marked by recurring seizures, is increasingly recognized for its intricate relationship with mitochondria, the cellular powerhouses responsible for energy production and calcium regulation. This review offers an in-depth examination of the interplay between epilepsy, mitochondrial function, and aging. Many factors might account for the correlation between epilepsy and aging. Mitochondria, integral to cellular energy dynamics and neuronal excitability, perform a critical role in the pathophysiology of epilepsy. The mechanisms linking epilepsy and mitochondria are multifaceted, involving mitochondrial dysfunction, reactive oxygen species (ROS), and mitochondrial dynamics. Mitochondrial dysfunction can trigger seizures by compromising ATP production, increasing glutamate release, and altering ion channel function. ROS, natural byproducts of mitochondrial respiration, contribute to oxidative stress and neuroinflammation, critical factors in epileptogenesis. Mitochondrial dynamics govern fusion and fission processes, influence seizure threshold and calcium buffering, and impact seizure propagation. Energy demands during seizures highlight the critical role of mitochondrial ATP generation in maintaining neuronal membrane potential. Mitochondrial calcium handling dynamically modulates neuronal excitability, affecting synaptic transmission and action potential generation. Dysregulated mitochondrial calcium handling is a hallmark of epilepsy, contributing to excitotoxicity. Epigenetic modifications in epilepsy influence mitochondrial function through histone modifications, DNA methylation, and non-coding RNA expression. Potential therapeutic avenues targeting mitochondria in epilepsy include mitochondria-targeted antioxidants, ketogenic diets, and metabolic therapies. The review concludes by outlining future directions in epilepsy research, emphasizing integrative approaches, advancements in mitochondrial research, and ethical considerations. Mitochondria emerge as central players in the complex narrative of epilepsy, offering profound insights and therapeutic potential for this challenging neurological disorder.



Proteomics profiling reveals lipid metabolism abnormalities during oogenesis in unexplained recurrent pregnancy loss

August 2024

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21 Reads

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1 Citation

Background Unexplained recurrent pregnancy loss (URPL) is a clinical dilemma in reproductive fields. Its diagnosis is mainly exclusionary after extensive clinical examination, and some of the patients may still face the risk of miscarriage. Methods We analyzed follicular fluid (FF) from in vitro fertilization (IVF) in eight patients with URPL without endocrine abnormalities or verifiable causes of abortion and eight secondary infertility controls with no history of pregnancy loss who had experienced at least one normal pregnancy and delivery by direct data-independent acquisition (dDIA) quantitative proteomics to identify differentially expressed proteins (DEPs). In this study, bioinformatics analysis was performed using online software including g:profiler, String, and ToppGene. Cytoscape was used to construct the protein–protein interaction (PPI) network, and ELISA was used for validation. Results Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the DEPs are involved in the biological processes (BP) of complement and coagulation cascades. Apolipoproteins (APOs) are key proteins in the PPI network. ELISA confirmed that APOB was low-expressed in both the FF and peripheral blood of URPL patients. Conclusion Dysregulation of the immune network intersecting coagulation and inflammatory response is an essential feature of URPL, and this disequilibrium exists as early as the oogenesis stage. Therefore, earlier intervention is necessary to prevent the development of URPL. Moreover, aberrant lipoprotein regulation appears to be a key factor contributing to URPL. The mechanism by which these factors are involved in the complement and coagulation cascade pathways remains to be further investigated, which also provides new candidate targets for URPL treatment.


Citations (39)


... However, the techniques used for exosome isolation vary widely. Common methods include ultracentrifugation, size exclusion chromatography (SEC), density gradient centrifugation and immunoaffinity capture [39][40][41][42]. Each method has its own advantages and limitations, particularly in terms of yield, purity and time efficiency. ...

Reference:

Exosomes in the Chemoresistance of Glioma: Key Point in Chemoresistance
Plant-Derived Exosomes in Therapeutic Nanomedicine, Paving the Path Toward Precision Medicine
  • Citing Article
  • September 2024

Phytomedicine

... Beyond model development, the lack of other data sources restricts the comparisons that can be drawn to more contemporary methods. Many of the latest lung cancer prediction models use data sources beyond symptoms and demographics, such as laboratory test results [38], imaging [39], and genetic testing [40]. Without access to such data, it is difficult to directly compare our model to such models. ...

Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study
  • Citing Article
  • September 2024

JMIR AI

... Evidence has suggested that mitochondria dysfunction is associated with a wide range of acute and chronic neurological disorders, including seizure occurrence and traumatic brain injury. Hence, its role in the complex narrative of epilepsy offers profound insights and is one of the crucial therapeutic targets against neurological disorders (Xie et al. 2024). This may not be unconnected with its function as the major source of endogenous free radicals (Méndez-Armenta et al. 2014;Singh et al. 2019). ...

Unraveling the nexus of age, epilepsy, and mitochondria: exploring the dynamics of cellular energy and excitability

... ITPR1 is key for calcium signaling, which is vital for oocyte maturation and early embryo development 42 . Other genes, like APOA1, involved in lipid metabolism, affect hormone synthesis and follicle health 43 . Collectively, these genes regulate key pathways related to cell survival, growth, angiogenesis, and signaling that are fundamental to ovarian function, oocyte competence, and successful embryo development. ...

Proteomics profiling reveals lipid metabolism abnormalities during oogenesis in unexplained recurrent pregnancy loss

... After incubation, a suitable antibiotic was added, and the culture was shaken for an additional 15 minutes before centrifugation at 6,000 rpm for 15 minutes. The cell pellet was discarded, and the supernatant was collected in a sterile flask and stored at 4°C until purification [12]. Before purification, the pH of the supernatant was adjusted to 3 by adding 1 N HCl. ...

Shaping the future of Gastrointestinal Cancers through Metabolic Interactions with Host Gut Microbiota

Heliyon

... i. Type 2 Diabetes: By improving insulin sensitivity and reducing blood glucose levels. [61] ii. Hyperlipidemia: By lowering LDL cholesterol and triglycerides. ...

Bioflavonoid combination attenuates diabetes-induced nephropathy in rats via modulation of MMP-9/TIMP-1, TGF-β, and GLUT-4-associated pathways

Heliyon

... Neuroblastoma is a common and aggressive pediatric cancer that originates in nerve tissues, responsible for a significant number of childhood cancer mortalities and presenting challenges for effective treatment due to its diverse genetic, morphological and clinical presentations; it most often begins in the adrenal glands but can also develop in other areas such as the neck, chest, abdomen, or spine (29). The effects of AXT were explored on SK-N-SH neuroblastoma cell line both in vitro and in vivo. ...

Revolutionizing pediatric neuroblastoma treatment: unraveling new molecular targets for precision interventions

... Хемокины класса CXC и хемокиновые рецепторы вызывают воспаление, инициирование и прогрессирование РЖ, способствуя ангиогенезу, трансформации опухоли, инвазии, метастатическому распространению и межклеточному взаимодействию [25]. Хемокиновые рецепторы CC могут быть использованы в качестве эффективного биомаркера для прогнозирования РЖ [26]. ...

Involvement of CXC chemokines (CXCL1-CXCL17) in gastric cancer: Prognosis and therapeutic molecules
  • Citing Article
  • January 2024

Life Sciences

... Impaired inflammatory responses have been identified as a significant factor in the pathomechanism of depression (e.g., Wang et al., 2024;Bielawski et al., 2023;Belzeaux et al., 2012). As mentioned above, depression-related neuroinflammation is connected to the excessive metabolic processes and oxidative stress occurrence (e.g., Correia et al., 2023). ...

Exploring the Pathophysiological Influence of Heme Oxygenase-1 on Neuroinflammation and Depression: A Study of Phytotherapeutic-Based Modulation
  • Citing Article
  • May 2024

Phytomedicine

... Recently, largescale language models have gained popularity, with models like Geneformer 46 , scBERT 47 , CellLM 48 , LangCell 49 , and scGPT 50 . Note that for unknown diseases or biological tissues, large models still need fine-tuning on reference datasets to achieve acceptable predictions 42,71,72 . We find that existing methods follow the trend of AI model development but lack consideration for the inductive bias and batch effects. ...

scFed: federated learning for cell type classification with scRNA-seq

Briefings in Bioinformatics