Key developments and hotspots of exosomes in Alzheimer’s disease: a bibliometric study spanning 2003 to 2023

BackgroundAlzheimer’s disease (AD) is a degenerative illness of the central nervous system that is irreversible and is characterized by gradual behavioral impairment and cognitive dysfunction. Researches on exosomes in AD have gradually gained the attention of scholars in recent years. However, the literatures in this research area do not yet have a comprehensive visualization analysis. The aim of this work is to use bibliometrics to identify the knowledge constructs and investigate the research frontiers and hotspots related to exosomes in AD.MethodsFrom January 2003 until June 2023, we searched the Web of Science Core Collection for literature on exosomes in AD. We found 585 papers total. The bibliometric study was completed using VOSviewer, the R package “bibliometrix,” and CiteSpace. The analysis covered nations, institutions, authors, journals, and keywords.ResultsFollowing 2019, the articles on exosomes in AD increased significantly year by year. The vast majority of publications came from China and the US. The University of California System, the National Institutes of Health, and the NIH National Institute on Aging in the US were the primary research institutions. Goetzl Edward J. was frequently co-cited, while Kapogiannis Dimitrios was the most prolific author in this discipline with the greatest number of articles. Lee Mijung et al. have been prominent in the last two years in exosomes in AD. The Journal of Alzheimer’s Disease was the most widely read publication, and Alzheimers & Dementia had the highest impact factor. The Journal of Biological Chemistry, Proceedings of the National Academy of Sciences of the United States of America, and Journal of Alzheimer’s Disease were the three journals with more than 1,000 citations. The primary emphasis of this field was Alzheimer’s disease, exosomes, and extracellular vesicles; since 2017, the number of phrases pertaining to the role of exosomes in AD pathogenesis has increased annually. “Identification of preclinical Alzheimer’s disease by a profile of pathogenic proteins in neurally derived blood exosomes: a case–control study” was the reference with the greatest citing power, indicating the future steered direction in this field.ConclusionUsing bibliometrics, we have compiled the research progress and tendencies on exosomes in Alzheimer’s disease for the first time. This helps determine the objectives and paths for future study

A brain tumor computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy

ObjectivesGliomas and brain metastases (Mets) are the most common brain malignancies. The treatment strategy and clinical prognosis of patients are different, requiring accurate diagnosis of tumor types. However, the traditional radiomics diagnostic pipeline requires manual annotation and lacks integrated methods for segmentation and classification. To improve the diagnosis process, a gliomas and Mets computer-aided diagnosis method with automatic lesion segmentation and ensemble decision strategy on multi-center datasets was proposed.MethodsOverall, 1,022 high-grade gliomas and 775 Mets patients’ preoperative MR images were adopted in the study, including contrast-enhanced T1-weighted (T1-CE) and T2-fluid attenuated inversion recovery (T2-flair) sequences from three hospitals. Two segmentation models trained on the gliomas and Mets datasets, respectively, were used to automatically segment tumors. Multiple radiomics features were extracted after automatic segmentation. Several machine learning classifiers were used to measure the impact of feature selection methods. A weight soft voting (RSV) model and ensemble decision strategy based on prior knowledge (EDPK) were introduced in the radiomics pipeline. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the classification performance.ResultsThe proposed pipeline improved the diagnosis of gliomas and Mets with ACC reaching 0.8950 and AUC reaching 0.9585 after automatic lesion segmentation, which was higher than those of the traditional radiomics pipeline (ACC:0.8850, AUC:0.9450).ConclusionThe proposed model accurately classified gliomas and Mets patients using MRI radiomics. The novel pipeline showed great potential in diagnosing gliomas and Mets with high generalizability and interpretability

Dynamic reconfiguration and transition of whole-brain networks in patients with MELAS revealed by a hidden Markov model

ObjectivesMitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes (MELAS) is a rare maternally inherited disease. The neuropathologic mechanisms and neural network alterations underlying stroke-like episodes (SLEs), a recurrent paroxysmal clinical event, remain unclear. The hidden Markov model (HMM) can detect profound alterations in neural activities across the whole-brain network.Materials and methodsWe initially collected data from a prospective cohort from 2019 to 2024. The confirmed diagnosis of MELAS was conducted through genetic testing or a muscle biopsy. Healthy control volunteers were recruited from the local community. By utilizing the HMM, we evaluated the temporal characteristics and transitions of HMM states and the specific community pattern of transitions and activation maps of the whole brain for subjects.ResultsThirty-six MELAS patients at the acute stage (MELAS-acute group) and 30 healthy controls (HC group) were included in this study. Based on HMM, fractional occupancies in states 5 and 6 for MELAS were significantly decreased (p < 0.001), but fractional occupancies in states 2, 3, 4, 7, 8, 9, 10, and 11 were significantly increased (p < 0.05), compared to HCs. The lifetimes of HMM states showed a similar decrease as fractional occupancies. The switching frequency of HMM states was significantly increased in MELAS (p < 0.001). Combined with the special community patterns of transitions, MELAS displayed differential activity patterns in crucial areas of the default mode network (DMN) and visual network (VN).ConclusionThis study suggests dynamic reconfiguration of HMM states, special transition modules, and multiple transition pathways in MELAS, providing novel insights into the neural network mechanisms underlying MELAS

A novel cascade machine learning pipeline for Alzheimer’s disease identification and prediction

IntroductionAlzheimer’s disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer’s disease, we built an Alzheimer’s segmentation and classification (AL-SCF) pipeline based on machine learning.MethodsIn our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve.ResultsOur proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification.DiscussionThe AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice

Peliminary exploration on the differential diagnosis between meningioma and schwannoma using contrast-enhanced T1WI flow-sensitive black-blood sequence

IntroductionContrast-enhanced T1WI flow-sensitive black-blood (CE-T1WI FSBB) is a newly developed sequence which had not been widely used for differential diagnosis of brain tumors.MethodsTo quantify the pre-operative imaging features of intratumoral microbleeds and intratumoral vessels using CE-T1WI FSBB scan and study the differences in biological behavior of meningiomas and schwannomas underlying the imaging features. Seventy-three cases of meningiomas and 24 cases of schwannomas confirmed by postoperative pathology were included. Two neuroradiologists independently counted intratumoral vessels and intratumoral microbleeds based on CE-T1WI FSBB images. The vessel density index (VDI) and microbleed density index (MDI) were the number of intratumoral vessels and the number of intratumoral microbleeds divided by the tumor volume, respectively. The consistency test of intratumoral vessel count and intratumoral microbleed count based on CE-T1WI FSBB were summarized using 2-way random intraclass correlation coefficients (ICC). Mann–Whitney U-test and chi-square test were used to determine significant differences between meningiomas and schwannomas, and fibrous meningiomas and epithelial meningiomas. P<0.05 was considered statistically significant.ResultsThe ICC of intratumoral vessels count and intratumoral microbleeds count were 0.89 and 0.99, respectively. There were significant differences in the number of intratumoral microbleeds (P<0.01) and MDI values (P<0.01) between meningiomas and schwannomas. There were no differences in the number of intratumoral vessels (P=0.64), VDI (P=0.17), or tumor volume (P=0.33). There were also differences in the number of intratumoral microbleeds (P<0.01), the MDI value (P<0.01), and the sex of patients (P<0.05) between fibrous meningiomas and epithelial meningiomas.DiscussionCE-T1WI FSBB can be a new technique for differentiating schwannomas from meningiomas, and even different types of meningiomas. Schwannomas have a higher incidence of intratumoral hemorrhage, more intratumoral microbleeds, and higher MDI values than meningiomas, which provides a new basis for preoperative differential diagnosis and treatment decisions

Differential expression of heat shock protein 90, 70, 60 in chicken muscles postmortem and its relationship with meat quality

Objective The aim of this study was to investigate the expression of heat shock protein (HSP) 90, 70, and 60 in chicken muscles and their possible relationship with quality traits of meat. Methods The breast muscles from one hundred broiler chickens were analyzed for drip loss and other quality parameters, and the levels of heat shock protein (HSP) 90, 70, and 60 were determined by immunoblots. Results Based on the data, chicken breast muscles were segregated into low (drip loss≤5%), intermediate (5%0.05). Conclusion Results of this study suggests that higher levels of HSP90 and HSP60 may be advantageous for maintenance of cell function and reduction of water loss, and they could act as potential indicator for better water holding capacity of meat

Principal Component Analysis and Cluster Analysis for Evaluating Free Amino Acids in Crayfish (Procambarus clarkii) from Different Co-culture Modes

To investigate the difference in the comprehensive quality of free amino acids (FAA) in crayfish meat from different co-culture modes, the FAA composition of crayfish tail meat from three representative co-culture modes in Xinghua city of Jiangsu province was determined, and the contribution of FAA to the taste of crayfish meat was evaluated by computing the taste active value (TAV). Comprehensive evaluation of FAA in crayfish meat was performed using principal component analysis (PCA) and cluster analysis. The results showed that 17 amino acids were found in crayfish meat from each co-culture mode, and the total amount of FAA was 21.80–27.11 mg/g. Arginine (Arg) was the most abundant FAA in all samples, accounting for 55.64%–67.76% of the total FAA, which was much more abundant than the other amino acids. Moreover, Arg contributed the most to the taste of crayfish meat. The TAV of the sweet amino acid alanine (Ala) and the bitter amino acid histidine (His) in crayfish were greater than 1 for all co-culture modes, indicating that both amino acids contributed to the taste of crayfish meat. The TAV of glutamic acid (Glu) as the amino acid with the strongest umami taste was greater than 1 only in crayfish meat from rice-crayfish mode with one-rice and two-crayfish in a field (RC2). Three principal components were extracted for the 17 amino acids, which cumulatively explained 89.937% of the total variance and could reflect the comprehensive information of amino acids in crayfish meat. The results of PCA showed that RC2 ranked first, and crayfish-crab mode (CC1) ranked last. The hierarchical cluster analysis divided crayfish meat from different co-culture modes into three categories. Similar results were obtained by PCA. This study demonstrated that the comprehensive quality of FAA in crayfish from rice-crayfish co-culture mode was better than that in crayfish from the other co-culture modes

MRI-based radiomics and deep learning model construction: non-invasive differentiation of molecular subtypes in primary intracranial diffuse large B-cell lymphoma

Background and purpose: Diffuse large B-cell lymphoma (DLBCL) is subclassified into germinal center B-cell-like (GCB) and non-GCB subtypes, which differ in prognosis and treatment response. However, current distinction still relies on invasive pathological assays. This study developed radiomics and deep-learning models based on multiparametric magnetic resonance imaging (MRI) to non-invasively differentiate the two subtypes preoperatively, thereby reducing dependence on histopathological examination. Methods: This study retrospectively included patients with pathologically confirmed DLBCL diagnosed at Huashan Hospital, Fudan University, and other institutions between March 2013 and December 2024. Using multiparametric MRI data, we developed DLBCL-subtype classification models that combined 4 radiomics-based machine-learning algorithms: support vector machine (SVM), logistic regression (LR), Gaussian process (GP) and Naive Bayes (NB), with 3 deep-learning architectures [densely-connected convolutional networks 121 (DenseNet121), residual network 101 (ResNet101) and EfficientNet-b5]. Additionally, two radiologists with different experience levels independently classified DLBCL on MRI in a blinded fashion. Model and radiologist performance were quantified using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), and F1-score to evaluate their ability to distinguish GCB from non-GCB subtypes. This study was approved by the Ethics Committee of Huashan Hospital of Fudan University (No. KY2024-663), and all patients signed informed consents. Results: A total of 173 patients were enrolled (55 with GCB subtype and 118 with non-GCB subtype). Radiomics and deep learning methods effectively distinguished DLBCL subtypes. Among these, the GP radiomics model (based on T1-CE+T2-FLAIR+ADC sequences) and DenseNet121 deep learning model (based on T1-CE+T2-FLAIR+ADC sequences) demonstrated optimal performance. Both achieved excellent results on the internal validation set (GP: AUC=0.900, ACC=0.896, F1=0.840; DenseNet121: AUC=0.846, ACC=0.854, F1=0.774) and maintained robustness on the external validation set. Furthermore, the classification efficacy of the optimal AI model surpassed that of experienced radiologists (highest physician AUC=0.678). Conclusion: Radiomics and deep-learning models based on multiparametric MRI features can effectively differentiate GCB from non-GCB subtypes of DLBCL. Among them, GP and DenseNet121 exhibit outstanding performance, especially when integrating multi-sequence feature sets for classifying DLBCL subtypes on complex imaging data

Resting-state functional connectivity changes within the default mode network and the salience network after antipsychotic treatment in early-phase schizophrenia

OBJECTIVE: Abnormal resting-state functional connectivity (FC), particularly in the default mode network (DMN) and the salience network (SN), has been reported in schizophrenia, but little is known about the effects of antipsychotics on these networks. The purpose of this study was to examine the effects of atypical antipsychotics on DMN and SN and the relationship between these effects and symptom improvement in patients with schizophrenia. METHODS: This was a prospective study of 33 patients diagnosed with schizophrenia and treated with antipsychotics at Shanghai Mental Health Center. Thirty-three healthy controls matched for age and gender were recruited. All subjects underwent functional magnetic resonance imaging (fMRI). Healthy controls were scanned only once; patients were scanned before and after 6–8 weeks of treatment. RESULTS: In the DMN, the patients exhibited increased FC after treatment in the right superior temporal gyrus, right medial frontal gyrus, and left superior frontal gyrus and decreased FC in the right posterior cingulate/precuneus (P<0.005). In the SN, the patients exhibited decreased FC in the right cerebellum anterior lobe and left insula (P<0.005). The FC in the right posterior cingulate/precuneus in the DMN negatively correlated with the difference between the Clinical Global Impression (CGI) score pre/post-treatment (r=−0.564, P=0.023) and negative trends with the difference in the Positive and Negative Syndrome Scale (PANSS) total score pre/post-treatment (r=−0.475, P=0.063) and the difference in PANSS-positive symptom scores (r=−0.481, P=0.060). CONCLUSION: These findings suggest that atypical antipsychotics could regulate the FC of certain key brain regions within the DMN in early-phase schizophrenia, which might be related to symptom improvement. However, the effects of atypical antipsychotics on SN are less clear