The brain extracellular matrix (ECM) consists of extremely long-lived proteins that assemble around neurons and synapses, to stabilize them. The ECM is thought to change only rarely, in relation to neuronal plasticity, through ECM proteolysis and renewed protein synthesis. We report here an alternative ECM remodeling mechanism, based on the recycling of ECM molecules. Using multiple ECM labeling and imaging assays, from super-resolution optical imaging to nanoscale secondary ion mass spectrometry, both in culture and in brain slices, we find that a key ECM protein, Tenascin-R, is frequently endocytosed, and later resurfaces, preferentially near synapses. The TNR molecules complete this cycle within ~3 days, in an activity-dependent fashion. Interfering with the recycling process perturbs severely neuronal function, strongly reducing synaptic vesicle exo- and endocytosis. We conclude that the neuronal ECM can be remodeled frequently through mechanisms that involve endocytosis and recycling of ECM proteins.
BACKGROUND: Differentiating gliomas and primary CNS lymphoma represents a diagnostic challenge with important therapeutic ramifications. Biopsy is the preferred method of diagnosis, while MR imaging in conjunction with machine learning has shown promising results in differentiating these tumors. PURPOSE: Our aim was to evaluate the quality of reporting and risk of bias, assess data bases with which the machine learning classification algorithms were developed, the algorithms themselves, and their performance. DATA SOURCES: Ovid EMBASE, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and the Web of Science Core Collection were searched according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.STUDY SELECTION: From 11,727 studies, 23 peer-reviewed studies used machine learning to differentiate primary CNS lymphoma from gliomas in 2276 patients.DATA ANALYSIS: Characteristics of data sets and machine learning algorithms were extracted. A meta-analysis on a subset of studies was performed. Reporting quality and risk of bias were assessed using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) and Prediction Model Study Risk Of Bias Assessment Tool. DATA SYNTHESIS:The highest area under the receiver operating characteristic curve (0.961) and accuracy (91.2%) in external validation were achieved by logistic regression and support vector machines models using conventional radiomic features. Meta-analysis of machine learning classifiers using these features yielded a mean area under the receiver operating characteristic curve of 0.944 (95% CI, 0.898-0.99). The median TRIPOD score was 51.7%. The risk of bias was high for 16 studies.LIMITATIONS: Exclusion of abstracts decreased the sensitivity in evaluating all published studies. Meta-analysis had high heterogeneity.CONCLUSIONS: Machine learning-based methods of differentiating primary CNS lymphoma from gliomas have shown great potential, but most studies lack large, balanced data sets and external validation. Assessment of the studies identified multiple deficiencies in reporting quality and risk of bias. These factors reduce the generalizability and reproducibility of the findings.ABBREVIATIONS: AI ¼ artificial intelligence; AUC ¼ area under the receiver operating characteristic curve; CNN ¼ convolutional neural network; ML ¼ machine learning; PCNSL ¼ primary CNS lymphoma; PRISMA ¼ Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PROBAST ¼ Prediction model study Risk Of Bias Assessment Tool; TRIPOD ¼ Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis G liomas are the most common primary malignancy of the CNS. 1 An important differential diagnosis for gliomas is primary CNS lymphoma (PCNSL), a more uncommon but highly malignant neoplasia. 2 Correct differentiation of these tumor entities is an important challenge for clinicians because
Purpose of reviewThis article reviews tau PET imaging with an emphasis on first-generation and second-generation tau radiotracers and their application in neurodegenerative disorders, including Alzheimer's disease and non-Alzheimer's disease tauopathies. Recent findingsTau is a critical protein, abundant in neurons within the central nervous system, which plays an important role in maintaining microtubules by binding to tubulin in axons. In its abnormal hyperphosphorylated form, accumulation of tau has been linked to a variety of neurodegenerative disorders, collectively referred to as tauopathies, which include Alzheimer's disease and non-Alzheimer's disease tauopathies [e.g., corticobasal degeneration (CBD), argyrophilic grain disease, progressive supranuclear palsy (PSP), and Pick's disease]. A number of first-generation and second-generation tau PET radiotracers have been developed, including the first FDA-approved agent [ 18 F]-flortaucipir, which allow for in-vivo molecular imaging of underlying histopathology antemortem, ultimately guiding disease staging and development of disease-modifying therapeutics. SummaryTau PET is an emerging imaging modality in the diagnosis and staging of tauopathies.
Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.
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