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PLAU plays a functional role in driving lung squamous cell carcinoma metastasis

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Genes & Diseases 2024年第2期11卷 554-557页

作者： Pengbo Ning Fuyu Du Xiaocheng Gong Jingtong Yuan Caixia Ding Xilian Zhao Wei Li School of Life Science and Technology Xidian UniversityXi'anShaanxi 710071China Engineering Research Center of Molecular & neuroimaging Ministry of EducationXi'anShaanxi 710071China department of Medical Oncology Zhongshan HospitalFudan UniversityShanghai 200032China department of Pathology Shaanxi Provincial Tumor HospitalXi'an Jiaotong UniversityXi'anShaanxi 710061China

The high mortality of patients with lung squamous cell carcinoma(LUSC)results from metastasis rather than primary tumors,1 whereas the molecular pathogenesis of cancer metastasis remains poorly understood.PLAU encodes urokinase-type plasminogen activator(uPA)which is closely related to tumor diagnosis,treatment target,and prognosis of patients.2.3 We previously predicted that PLAU is a key gene negatively associated with overall survival by integrated multidimensional analyses of The Cancer Genome Atlas(TCGA)datasets from 504 samples of LUSC tumor tissues and 46 samples of adjacent non-tumorous lung tissues.4 However,whether the role of PLAU contributes to the metastasis of LUC remains unclear.Here,we analyzed the association between uPA levels and LUSC and found that uPA levels were associated with pathological variations in LUSC patients.

关键词： metastasis lung squamous

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

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Neural Regeneration Research 2024年第3期19卷 663-670页

作者： Yang Guo Liying Sun Wenyao Zhong Nan Zhang Zongxuan Zhao Wen Tian department of Hand Surgery Beijing Jishuitan HospitalBeijingChina

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future re

关键词： artificial intelligence artificial prosthesis medical-industrial integration brain-machine interface deep learning machine learning networked hand prosthesis neural interface neural network neural regeneration peripheral nerve

neuroimaging in atypical normal tension glaucoma:debating routine implementation in the absence of classic neurological findings

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International Journal of Ophthalmology(English edition) 2024年第3期17卷 509-517页

作者： Umay Güvenç Gülizar Demirok Güner Üney Selma Uzman Ophthalmology department Ankara Training and Research HospitalAnkara 06230Turkey

●AIM:To assess the necessity of neuroimaging in patients with neurological or atypical findings of normal tension glaucoma(NTG)who do not exhibit typical glaucoma manifestations.●METHODS:A retrospective analysis was conducted on 90 atypical NTG patients who underwent cranial magnetic resonance imaging(MRI)due to atypical symptoms.The demographic characteristics,clinical parameters,and radiological findings were recorded.●RESULTS:Among the patients,66.7%had abnormal radiology results,with the most common findings being gliosis(34.4%),sequelae of cerebrovascular events and vascular malformations(14.4%),and benign intracranial mass lesions(11%).Non-glaucomatous visual field defects were more frequently observed in patients with abnormal neuroimaging results.However,there were no significant differences in intraocular pressure,optic disc parameters,retinal nerve fiber layer thickness,and visual field indices between patients with normal and abnormal radiological results.The mean age of the patients was 58.74y.Interestingly,there was a significant age difference,with the abnormal radiology group having a higher median age(P=0.021).●CONCLUSION:The study highlights the importance of cranial imaging in older NTG patients to detect underlying pathologies and prevent misdiagnosis.It suggests that neuroimaging may be warranted in NTG patients with atypical visual field defects incompatible with glaucoma.However,routine neuroimaging in all NTG patients without classic neurological signs may not be necessary.

关键词： normal tension glaucoma neuroimaging atypical visual field defects

inMTSCCA:An Integrated Multi-task Sparse Canonical Correlation Analysis for Multi-omic Brain Imaging Genetics

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Genomics, Proteomics & Bioinformatics 2023年第2期21卷 396-413页

作者： Lei Du Jin Zhang Ying Zhao Muheng Shang Lei Guo Junwei Han The Alzheimer's Disease neuroimaging Initiative department of Intelligent Science and Technology School of AutomationNorthwestern Polytechnical UniversityXi'an 710072China

Identifying genetic risk factors for Alzheimer's disease(AD)is an important research topic.To date,different endophenotypes,such as imaging-derived endophenotypes and proteomic expression-derived endophenotypes,have shown the great value in uncovering risk genes compared to case-control studies.Biologically,a co-varying pattern of different omics-derived endophenotypes could result from the shared genetic basis.However,existing methods mainly focus on the effect of endophenotypes alone;the effect of cross-endophenotype(CEP)associations remains largely unexploited.In this study,we used both endophenotypes and their CEP associations of multi-omic data to identify genetic risk factors,and proposed two integrated multi-task sparse canonical correlation analysis(inMTSCCA)methods,i.e.,pairwise endophenotype correlationguided MTSCCA(pcMTSCCA)and high-order endophenotype correlation-guided MTSCCA(hocMTSCCA).pcMTSCCA employed pairwise correlations between magnetic resonance imaging(MRI)-derived,plasma-derived,and cerebrospinal fluid(CSF)-derived endophenotypes as an additional penalty.hocMTSCCA used high-order correlations among these multi-omic data for regularization.To figure out genetic risk factors at individual and group levels,as well as altered endophenotypic markers,we introduced sparsity-inducing penalties for both models.We compared pcMTSCCA and hocMTSCCA with three related methods on both simulation and real(consisting of neuroimaging data,proteomic analytes,and genetic data)datasets.The results showed that our methods obtained better or comparable canonical correlation coefficients(CCCs)and better feature subsets than benchmarks.Most importantly,the identified genetic loci and heterogeneous endophenotypic markers showed high relevance.Therefore,jointly using multi-omic endophenotypes and their CEP associations is promising to reveal genetic risk factors.

关键词： Brain imaging genetics Multi-omic endophenotype Cross-endophenotype association Genetic risk factor Medical imageanalysis

Magnetic resonance spectroscopy findings of brain olfactory areas in patients with COVID-19-related anosmia:A preliminary comparative study

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World Journal of Otorhinolaryngology-Head and Neck Surgery 2024年第2期10卷 105-112页

作者： Shadman Nemati Mohammad Haghani Dogahe Alia Saberi Naghi Ramezani Pejman Kiani Tofigh Yaghubi Kalurazi Ehsan Kazemnejad Leili Sara Seddighi Abbas Monsef department of Otolaryngology and Head and Neck Surgery School of MedicineOtorhinolaryngology Research CenterGuilan University of Medical SciencesRashtIran Guilan Road Trauma Research Center Guilan University of Medical SciencesRashtGuilanIran department of Neurology Neuroscience Research CenterPoursina HospitalGuilan University of Medical SciencesRashtIran department of Radiology Pars International HospitalGuilan ProvinceIran department of Neuroscience and Addiction Studies School of Advanced Technologies in MedicineTehranIran department of Infectious Diseases Razi Clinical Research Development UnitRazi HospitalGuilan University of Medical SciencesRashtIran Center of Magnetic Resonance Research Department of Radiation OncologyUniversity of Minnesota Medical SchoolMinneapolisMinnesotaUSA

Objectives:2019 novel coronavirus disease(COVID-19)infection is commonly associated with olfactory dysfunctions,but the basic pathogenesis of these complications remains controversial.This study seeks to evaluate the value of magnetic resonance spectroscopy(MRS)in determining the molecular neurometabolite alterations within the main brain olfactory areas in patients with COVID-19-related anosmia.Methods:In a cross-sectional study,seven patients with persistent COVID-19-related anosmia(mean age:29.57 years)and seven healthy volunteers(mean age:27.28 years)underwent MRS in which N-acetyl-aspartate(NAA),choline(Cho),creatine(Cr),and their ratios were measured in the anterior cingulate cortex,dorsolateral prefrontal cortex,orbitofrontal cortex(OFC),insular cortex,and ventromedial prefrontal cortex.Data were analyzed using TARQUIN software(version 4.3.10),and the results were compared with an independent sample t-test and nonparametric Mann-Whitney test based on the normality of the MRS data distribution.Results:The mean duration of anosmia before imaging was 8.5 months in COVID-19-related anosmia group.MRS analysis elucidated a significant association between MRS findings within OFC and COVID-19-related anosmia(P_(disease)<0.01),and NAA was among the most important neurometabolites(P_(interaction)=0.006).Reduced levels of NAA(P<0.001),Cr(P<0.001)and^(NAA)/Cho ratio(P=0.007)within OFC characterize COVID-19-related anosmia.Conclusions:This study emphasizes that MRS can be illuminating in COVID-19-related anosmia and indicates a possible association between central nervous system impairment and persistent COVID-19-related anosmia.

关键词： anosmia COVID-19 magnetic resonance spectroscopy neuroimaging olfaction

Neural plasticity in Parkinson's disease: a neuroimaging perspective

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Neural Regeneration Research 2024年第6期19卷 1203-1205页

作者： Christina Andica Koji Kamagata department of Radiology Juntendo University Graduate School of Medicine Faculty of Health Data Science Juntendo University

Parkinson’s disease(PD) is a progressive neurodegenerative disorder characterized primarily by classical motor signs of bradykinesia,rigidity,tremors,and postural instability usually manifesti ng unilate rally or at least asymmetrically.The disease involves a loss of dopaminergic neurons projecting from the substantia nigra pars compacta to the dorsal striatum,

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Association of creatine kinase with Alzheimer’s disease pathology:A cross-sectional study

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Chinese Medical Journal 2024年第1期137卷 117-119页

作者： Yutong Wang Peiyang Gao Hongman Liu Tingyu Wang Jiaming Fan Tingbo Jiang Alzheimer’s Disease neuroimaging Initiative(ADNI) Beijing Hospital National Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100005China department of Cardiology The First Affiliated Hospital of Soochow UniversitySuzhouJiangsu 215000China Institute for Cardiovascular Science of Soochow University SuzhouJiangsu 215000China department of Neurology Qingdao Municipal HospitalQingdao UniversityQingdaoShandong 266071China Cardiac Function department the Affiliated Taian City Central Hospital of Qingdao UniversityTaianShandong 271000China department of Cardiac Surgery The First Affiliated Hospital of Soochow UniversitySuzhouJiangsu 215000China 不详

To the Editor:Creatine kinase(CK),also called creatine phosphokinase,is primarily located in the cytoplasm and mitochondria.It can be used to diagnose cardiac diseases such as myocardial infarction,viral myocarditis,and pericarditis because it is a vital kinase directly related to intracellular energy operation,muscle contraction,and adenosine triphosphate(ATP)regeneration.Several studies have confirmed in recent years that serum biomarkers such as elevated CK are associated with Alzheimer’s disease(AD)and its related pathological protein.[1]Previous research indicated that systemic conditions,particularly cardiovascular and cerebrovascular diseases,are significant risk factors for cognitive impairment,and dysfunction of peripheral organs may also contribute to cognitive decline and AD.[2]However,the connection between CK and AD remains unknown.Therefore,to investigate the association between CK and cerebrospinal fluid(CSF)AD pathology biomarkers,we analyzed data from the Alzheimer’s Disease neuroimaging Initiative(ADNI),a global multi-centered database.

关键词： Alzheimer pathology organs

The Application of Artificial Intelligence in Alzheimer’s Research

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Tsinghua Science and Technology 2024年第1期29卷 13-33页

作者： Qing Zhao Hanrui Xu Jianqiang Li Faheem Akhtar Rajput Liyan Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Medicine Tsinghua UniversityBeijing 100084China department of Computer Science Sukkur IBA UniversitySukkur 65200Pakistan department of Neurology Tsinghua University Yuquan HospitalBeijing 100040China

Alzheimer's disease(AD)is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function,but the etiology of AD is still unclear.With the explosive growth of electronic health data,the application of artificial inteligence(Al)in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches,and improving the disease's diagnostic and prognostic outcome.This paper first briefly introduces Al technologies and applications in medicine,and then presents a comprehensive review of Al in AD.In simple,it includes etiology discovery based on genetic data,computer-aided diagnosis(CAD),computer-aided prognosis(CAP)of AD using multi-modality data(genetic,neuroimaging and linguistic data),and pharmacological or non-pharmacological approaches for treating AD.Later,some popular publicly available AD datasets are introduced,which are important for advancing Al technologies in AD analysis.Finally,core research challenges and future research directions are discussed.

关键词： Alzheimer's disease artificial intelligence etiology discovery computer-aided diagnosis computer-aided prognosis treatment

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

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Neural Regeneration Research 2024年第12期19卷 2637-2648页

作者： Yi Guo Zhonghua Lin Zhen Fan Xin Tian department of Neurology Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Sichuan Provincial Center for Mental Health Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina department of Geriatrics Sichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina department of Neurology Chongqing Key Laboratory of NeurologyThe First Affiliated Hospital of Chongqing Medical UniversityChongqingChina

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

关键词： electrophysiological techniques epilepsy functional brain network functional magnetic resonance imaging functional near-infrared spectroscopy machine leaning molecular imaging neuroimaging techniques structural brain network virtual epileptic models