Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obs...Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.展开更多
Parkinson＇s disease （PD） is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigr...Parkinson＇s disease （PD） is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigrostriatal neurons are degenerated and 80% of content of the striatal dopamine is reduced before the diagnosis can be established according to widely accepted clinical diagnostic criteria. This condition describes a stage of disease called ＂prodromal＂, where non-motor symptoms, such as olfactory dysfunction, constipation, rapid eye movement behaviour disorder, depression, precede motor sign of PD. Detection of prodromal phase of PD is becoming an important goal for determining the prognosis and choosing a suitable treatment strategy. In this review, we present some non-invasive instrumental approaches that could be useful to identify patients in the prodromal phase of PD or in an early clinical phase, when the first motor symptoms begin to be apparent. Conventional magnetic resonance imaging （MRI） and advanced MRI techniques, such as magnetic resonance spectroscopy imaging, diffusion-weighted and diffusion tensor imaging and functional MRI, are useful to differentiate early PD with initial motor symptoms from atypical parkinsonian disorders, thus, making easier early diagnosis. Functional MRI and diffusion tensor imaging techniques can show abnormalities in the olfactory system in prodromal PD.展开更多
Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was under...Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous studies.In this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2*mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019.According to the inclusion time,123 PD patients and 121 healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation dataset.We extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset.The informative features so identified were then used to construct a diagnostic model for PD.The constructed model contained 36 informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training dataset.On the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same 36 informative features were 80.3±7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.3%and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,respectively.The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8±6.9%and 79.1±6.5%,respectively.In conclusion,the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.展开更多
The early diagnosis of Parkinson’s disease(PD)provides opportunities for early intervention to slow the progression of neurological degeneration in patients,particularly as the aging population increases in our socie...The early diagnosis of Parkinson’s disease(PD)provides opportunities for early intervention to slow the progression of neurological degeneration in patients,particularly as the aging population increases in our society.Among a series of pathological features of PD,mitochondria abnormalities have been identified as central event that occurs at the early stage of PD.However,the method for detecting mitochondrial abnormalities-associated early PD has not been fully developed.We herein report a specifically mitochondrial targeting probe(named TPA-BT-SCP)that is able to characterize mitochondria abnormalities for early diagnosis of PD and monitor PD neurodegenerative progress.The probe is an aggregation-induced emission(AIE)probe with a strong positive charge,a 3D distorted molecular structure,and a separated HOMO-LUMO distribution,designed with unique molecular design guidelines.Our research demonstrated that TPA-BT-SCP could emit stable and strong fluorescence,and rapidly accumulate in mitochondria due to the negative charge.After intranasal administration of 1-methy-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced PD mice,TPA-BT-SCP successfully bypassed the blood−brain barrier to light up the brain,allowing the grading of PD severity based on its high sensitivity.Taken together,this work develops a novel AIE probe that exhibits dramatically high sensitivity to mitochondrial changes and enables noninvasive diagnosis of early PD in the brain.展开更多
Multiple system atrophy is a sporadic,progressive,adult-onset,neurodegenerative disorder characte rized by autonomic dysfunction symptoms,parkinsonian features,and cerebellar signs in va rious combinations.An early di...Multiple system atrophy is a sporadic,progressive,adult-onset,neurodegenerative disorder characte rized by autonomic dysfunction symptoms,parkinsonian features,and cerebellar signs in va rious combinations.An early diagnosis of multiple system atrophy is of utmost impo rtance for the proper prevention and management of its potentially fatal complications leading to the poor prognosis of these patients.The current diagnostic criteria incorporate several clinical red flags and magnetic resonance imaging marke rs supporting diagnosis of multiple system atrophy.Nonetheless,especially in the early disease stage,it can be challenging to differentiate multiple system atrophy from mimic disorders,in particular Parkinson’s disease.Electromyography of the external anal sphincter represents a useful neurophysiological tool for diffe rential diagnosis since it can provide indirect evidence of Onuf’s nucleus degeneration,which is a pathological hallmark of multiple system atrophy.However,the diagnostic value of external anal sphincter electromyography has been a matter of debate for three decades due to controve rsial reports in the literature.In this review,after a brief ove rview of the electrophysiological methodology,we first aimed to critically analyze the available knowledge on the diagnostic role of external anal sphincter electromyography.We discussed the conflicting evidence on the clinical correlations of neurogenic abnormalities found at external anal sphincter electro myography.Finally,we repo rted recent prognostic findings of a novel classification of electromyography patterns of the external anal sphincter that could pave the way toward the implementation of this neurophysiological technique for survival prediction in patients with multiple system atrophy.展开更多
Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while ...Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.展开更多
In recent years,Parkinson’s Disease(PD)as a progressive syndrome of the nervous system has become highly prevalent worldwide.In this study,a novel hybrid technique established by integrating a Multi-layer Perceptron ...In recent years,Parkinson’s Disease(PD)as a progressive syndrome of the nervous system has become highly prevalent worldwide.In this study,a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network(MLP)with the Biogeography-based Optimization(BBO)to classify PD based on a series of biomedical voice measurements.BBO is employed to determine the optimal MLP parameters and boost prediction accuracy.The inputs comprised of 22 biomedical voice measurements.The proposed approach detects two PD statuses:0-disease status and 1-good control status.The performance of proposed methods compared with PSO,GA,ACO and ES method.The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection.The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms,and consequently,it served as a promising new robust tool with excellent PD diagnosis performance.展开更多
Parkinson＇s disease （PD） is the second most common age-related neurodegenerative disorder after Alzheimer＇s disease. To date, the clinical diagnosis of PD is primarily based on the late onset of motor impairments....Parkinson＇s disease （PD） is the second most common age-related neurodegenerative disorder after Alzheimer＇s disease. To date, the clinical diagnosis of PD is primarily based on the late onset of motor impairments. Unfortunately, at this stage, most of the dopaminergic neurons may have already been lost, leading to the limited clinical benefits of current therapeutics. Therefore, early identification of PD, especially at the prodromal stage, is still a main challenge in the diagnosis and management of this disease. Recently, microRNAs （miRNAs） in cerebrospinal fluid or peripheral blood have been proposed as putative biomarkers to assist in PD diagnosis and therapy. In this review, we systematically summarize the changes of miRNA expression profiles in PD patients, and highlight their putative roles in the diagnosis and treatment of this devastating disease.展开更多
Neurodegenerative diseases are a heterogeneous group of maladies, characterized by progressive loss of neurons. These diseases involve an intricate pattern of cross-talk between different types of cells to maintain sp...Neurodegenerative diseases are a heterogeneous group of maladies, characterized by progressive loss of neurons. These diseases involve an intricate pattern of cross-talk between different types of cells to maintain specific signaling pathways. A component of such intercellular cross-talk is the exchange of various types of extracellular vesicles (EVs). Exosomes are a subset of EVs, which are increasingly being known for the role they play in the pathogenesis and progression of neurodegenerative diseases, e.g., synucleinopathies and tauopathies. The ability of the central nervous system exosomes to cross the blood-brain barrier into blood has generated enthusiasm in their study as potential biomarkers. However, the lack of standardized, efficient, and ultra-sensitive methods for the isolation and detection of brain-derived exosomes has hampered the development of effective biomarkers. Exosomes mirror heterogeneous biological changes that occur during the progression of these incurable illnesses, potentially offering a more comprehensive outlook of neurodegenerative disease diagnosis, progression and treatment. In this review, we aim to discuss the challenges and opportunities of peripheral biofluid-based brain-exosomes in the diagnosis and biomarker discovery of Alzheimer’s and Parkinson’s diseases. In the later part, we discuss the traditional and emerging methods used for the isolation of exosomes and compare their advantages and disadvantages in clinical settings.展开更多
The number and health burden of Parkinson’s disease increase rapidly in China.It is estimated that China will have nearly half of the Parkinson’s disease population in the world in 2030.In this review,we present an ...The number and health burden of Parkinson’s disease increase rapidly in China.It is estimated that China will have nearly half of the Parkinson’s disease population in the world in 2030.In this review,we present an overview of epidemiology and health economics status of Parkinson’s disease across China and discuss the risk factors of Parkinson’s disease and related complications.From the view of clinical research,we also discuss the current status of clinical trials,diagnostic biomarkers,treatment of Parkinson’s disease,tertiary network and post-occupation education in Chinese Parkinson’s disease clinics.展开更多
文摘Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.
文摘Parkinson＇s disease （PD） is a neurodegenerative disorder characterized by selective and progressive degeneration, as well as loss of dopaminergic neurons in the substantia nigra. In PD, approximately 60-70% of nigrostriatal neurons are degenerated and 80% of content of the striatal dopamine is reduced before the diagnosis can be established according to widely accepted clinical diagnostic criteria. This condition describes a stage of disease called ＂prodromal＂, where non-motor symptoms, such as olfactory dysfunction, constipation, rapid eye movement behaviour disorder, depression, precede motor sign of PD. Detection of prodromal phase of PD is becoming an important goal for determining the prognosis and choosing a suitable treatment strategy. In this review, we present some non-invasive instrumental approaches that could be useful to identify patients in the prodromal phase of PD or in an early clinical phase, when the first motor symptoms begin to be apparent. Conventional magnetic resonance imaging （MRI） and advanced MRI techniques, such as magnetic resonance spectroscopy imaging, diffusion-weighted and diffusion tensor imaging and functional MRI, are useful to differentiate early PD with initial motor symptoms from atypical parkinsonian disorders, thus, making easier early diagnosis. Functional MRI and diffusion tensor imaging techniques can show abnormalities in the olfactory system in prodromal PD.
基金supported by the National Natural Science Foundation of China, Nos.82001767(to XJG), 81971577(to MMZ), 82171888(to XJX)the Natural Science Foundation of Zhejiang Province of China, Nos.LQ21H180008(to XJG), LQ20H180012(to MX)+1 种基金the China Postdoctoral Science Foundation, Nos.2021T140599(to XJG), 2019M662082(to XJG)the 13th Five-year Plan for National Key Research and Development Program of China, No.2016YFC1306600(to MMZ)
文摘Brain radiomics can reflect the characteristics of brain pathophysiology.However,the value of T1-weighted images,quantitative susceptibility mapping,and R2*mapping in the diagnosis of Parkinson’s disease(PD)was underestimated in previous studies.In this prospective study to establish a model for PD diagnosis based on brain imaging information,we collected high-resolution T1-weighted images,R2*mapping,and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019.According to the inclusion time,123 PD patients and 121 healthy controls were assigned to train the diagnostic model,while the remaining 106 subjects were assigned to the external validation dataset.We extracted 1408 radiomics features,and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset.The informative features so identified were then used to construct a diagnostic model for PD.The constructed model contained 36 informative radiomics features,mainly representing abnormal subcortical iron distribution(especially in the substantia nigra),structural disorganization(e.g.,in the inferior temporal,paracentral,precuneus,insula,and precentral gyri),and texture misalignment in the subcortical nuclei(e.g.,caudate,globus pallidus,and thalamus).The predictive accuracy of the established model was 81.1±8.0%in the training dataset.On the external validation dataset,the established model showed predictive accuracy of 78.5±2.1%.In the tests of identifying early and drug-naïve PD patients from healthy controls,the accuracies of the model constructed on the same 36 informative features were 80.3±7.1%and 79.1±6.5%,respectively,while the accuracies were 80.4±6.3%and 82.9±5.8%for diagnosing middle-to-late PD and those receiving drug management,respectively.The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8±6.9%and 79.1±6.5%,respectively.In conclusion,the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.
基金National Natural Science Foundation of China,Grant/Award Numbers:82241058,31922045,31771031Natural Science Foundation of Tianjin Province of China,Grant/Award Number:21JCZDJC00290+2 种基金State Key Laboratory of Medicinal Chemical Biology in Nankai University,Grant/Award Number:2020017State Key Laboratory of Biochemical EngineeringOpen Funding Project of State Key Laboratory of Biochemical Engineering,Grant/Award Number:2021KF-01。
文摘The early diagnosis of Parkinson’s disease(PD)provides opportunities for early intervention to slow the progression of neurological degeneration in patients,particularly as the aging population increases in our society.Among a series of pathological features of PD,mitochondria abnormalities have been identified as central event that occurs at the early stage of PD.However,the method for detecting mitochondrial abnormalities-associated early PD has not been fully developed.We herein report a specifically mitochondrial targeting probe(named TPA-BT-SCP)that is able to characterize mitochondria abnormalities for early diagnosis of PD and monitor PD neurodegenerative progress.The probe is an aggregation-induced emission(AIE)probe with a strong positive charge,a 3D distorted molecular structure,and a separated HOMO-LUMO distribution,designed with unique molecular design guidelines.Our research demonstrated that TPA-BT-SCP could emit stable and strong fluorescence,and rapidly accumulate in mitochondria due to the negative charge.After intranasal administration of 1-methy-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP)-induced PD mice,TPA-BT-SCP successfully bypassed the blood−brain barrier to light up the brain,allowing the grading of PD severity based on its high sensitivity.Taken together,this work develops a novel AIE probe that exhibits dramatically high sensitivity to mitochondrial changes and enables noninvasive diagnosis of early PD in the brain.
基金supported by the Italian Ministry of Health (’Ricerca Corrente’2020-2021)(to MT)。
文摘Multiple system atrophy is a sporadic,progressive,adult-onset,neurodegenerative disorder characte rized by autonomic dysfunction symptoms,parkinsonian features,and cerebellar signs in va rious combinations.An early diagnosis of multiple system atrophy is of utmost impo rtance for the proper prevention and management of its potentially fatal complications leading to the poor prognosis of these patients.The current diagnostic criteria incorporate several clinical red flags and magnetic resonance imaging marke rs supporting diagnosis of multiple system atrophy.Nonetheless,especially in the early disease stage,it can be challenging to differentiate multiple system atrophy from mimic disorders,in particular Parkinson’s disease.Electromyography of the external anal sphincter represents a useful neurophysiological tool for diffe rential diagnosis since it can provide indirect evidence of Onuf’s nucleus degeneration,which is a pathological hallmark of multiple system atrophy.However,the diagnostic value of external anal sphincter electromyography has been a matter of debate for three decades due to controve rsial reports in the literature.In this review,after a brief ove rview of the electrophysiological methodology,we first aimed to critically analyze the available knowledge on the diagnostic role of external anal sphincter electromyography.We discussed the conflicting evidence on the clinical correlations of neurogenic abnormalities found at external anal sphincter electro myography.Finally,we repo rted recent prognostic findings of a novel classification of electromyography patterns of the external anal sphincter that could pave the way toward the implementation of this neurophysiological technique for survival prediction in patients with multiple system atrophy.
基金supported by the Deanship of Scientific Research at Prince Sattam bin Aziz University under the Research Project (PSAU/2023/01/22425).
文摘Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.
文摘In recent years,Parkinson’s Disease(PD)as a progressive syndrome of the nervous system has become highly prevalent worldwide.In this study,a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network(MLP)with the Biogeography-based Optimization(BBO)to classify PD based on a series of biomedical voice measurements.BBO is employed to determine the optimal MLP parameters and boost prediction accuracy.The inputs comprised of 22 biomedical voice measurements.The proposed approach detects two PD statuses:0-disease status and 1-good control status.The performance of proposed methods compared with PSO,GA,ACO and ES method.The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection.The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms,and consequently,it served as a promising new robust tool with excellent PD diagnosis performance.
基金supported by the National Natural Science Foundation of China (81430021 and 81370470)
文摘Parkinson＇s disease （PD） is the second most common age-related neurodegenerative disorder after Alzheimer＇s disease. To date, the clinical diagnosis of PD is primarily based on the late onset of motor impairments. Unfortunately, at this stage, most of the dopaminergic neurons may have already been lost, leading to the limited clinical benefits of current therapeutics. Therefore, early identification of PD, especially at the prodromal stage, is still a main challenge in the diagnosis and management of this disease. Recently, microRNAs （miRNAs） in cerebrospinal fluid or peripheral blood have been proposed as putative biomarkers to assist in PD diagnosis and therapy. In this review, we systematically summarize the changes of miRNA expression profiles in PD patients, and highlight their putative roles in the diagnosis and treatment of this devastating disease.
文摘Neurodegenerative diseases are a heterogeneous group of maladies, characterized by progressive loss of neurons. These diseases involve an intricate pattern of cross-talk between different types of cells to maintain specific signaling pathways. A component of such intercellular cross-talk is the exchange of various types of extracellular vesicles (EVs). Exosomes are a subset of EVs, which are increasingly being known for the role they play in the pathogenesis and progression of neurodegenerative diseases, e.g., synucleinopathies and tauopathies. The ability of the central nervous system exosomes to cross the blood-brain barrier into blood has generated enthusiasm in their study as potential biomarkers. However, the lack of standardized, efficient, and ultra-sensitive methods for the isolation and detection of brain-derived exosomes has hampered the development of effective biomarkers. Exosomes mirror heterogeneous biological changes that occur during the progression of these incurable illnesses, potentially offering a more comprehensive outlook of neurodegenerative disease diagnosis, progression and treatment. In this review, we aim to discuss the challenges and opportunities of peripheral biofluid-based brain-exosomes in the diagnosis and biomarker discovery of Alzheimer’s and Parkinson’s diseases. In the later part, we discuss the traditional and emerging methods used for the isolation of exosomes and compare their advantages and disadvantages in clinical settings.
基金This work was supported by grants from the National Natural Science Foundation of China[grant numbers 81430022,91332107,81371407].
文摘The number and health burden of Parkinson’s disease increase rapidly in China.It is estimated that China will have nearly half of the Parkinson’s disease population in the world in 2030.In this review,we present an overview of epidemiology and health economics status of Parkinson’s disease across China and discuss the risk factors of Parkinson’s disease and related complications.From the view of clinical research,we also discuss the current status of clinical trials,diagnostic biomarkers,treatment of Parkinson’s disease,tertiary network and post-occupation education in Chinese Parkinson’s disease clinics.
文摘目的探讨帕金森病(Parkinson’s disease,PD)患者区别于正常人的血清蛋白质差异表达。方法选择原发性PD患者35例和正常人35例,用弱阳离子交换(weak cationic exchange,WCX)磁珠捕获血清蛋白质组分,用MALDI-TOF-MS(matrix assisted laser desorption/ionization time of flight mass spectrometer)检测各样品的蛋白质质谱,统计学筛选差异表达分子,监督神经网络算法建立区分模型,盲法验证。结果在PD组和对照组之间筛查到8个差异分子(非参数检验Z值范围为-4.458～-3.059,P<0.05)。以监督神经网络算法建立区分模型,其判断正确率为81.4%。对25例新样本的盲法验证结果显示,模型的正确率为76.0%。结论PD患者血清蛋白质的表达谱有别于正常人。蛋白质组学数据结合生物信息学方法可能有助于PD的诊断。
