Apolipoprotein E ε4 accelerates the longitudinal cerebral atrophy in open access series of imaging studies-3 elders without dementia at enrollment

IntroductionEarly studies have reported that APOE is strongly associated with brain atrophy and cognitive decline among healthy elders and Alzheimer’s disease (AD). However, previous research has not directly outlined the modulation of APOE on the trajectory of cerebral atrophy with aging during the conversion from cognitive normal (CN) to dementia (CN2D).MethodsThis study tried to elucidate this issue from a voxel-wise whole-brain perspective based on 416 qualified participants from a longitudinal OASIS-3 neuroimaging cohort. A voxel-wise linear mixed-effects model was applied for detecting cerebrum regions whose nonlinear atrophic trajectories were driven by AD conversion and to elucidate the effect of APOE variants on the cerebral atrophic trajectories during the process.ResultsWe found that CN2D participants had faster quadratically accelerated atrophy in bilateral hippocampi than persistent CN. Moreover, APOE ε4 carriers had faster-accelerated atrophy in the left hippocampus than ε4 noncarriers in both CN2D and persistent CN, and CN2D ε4 carriers an noncarriers presented a faster atrophic speed than CN ε4 carriers. These findings could be replicated in a sub-sample with a tough match in demographic information.DiscussionOur findings filled the gap that APOE ε4 accelerates hippocampal atrophy and the conversion from normal cognition to dementia

Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model

IntroductionAccurately and objectively quantifying the clinical features of Parkinson's disease (PD) is crucial for assisting in diagnosis and guiding the formulation of treatment plans. Therefore, based on the data on multi-site motor features, this study aimed to develop an interpretable machine learning (ML) model for classifying the “OFF” and “ON” status of patients with PD, as well as to explore the motor features that are most associated with changes in clinical symptoms.MethodsWe employed a support vector machine with a recursive feature elimination (SVM-RFE) algorithm to select promising motion features. Subsequently, 12 ML models were constructed based on these features, and we identified the model with the best classification performance. Then, we used the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model agnostic Explanations (LIME) methods to explain the model and rank the importance of those motor features.ResultsA total of 96 patients were finally included in this study. The naive Bayes (NB) model had the highest classification performance (AUC = 0.956; sensitivity = 0.8947, 95% CI 0.6686–0.9870; accuracy = 0.8421, 95% CI 0.6875–0.9398). Based on the NB model, we analyzed the importance of eight motor features toward the classification results using the SHAP algorithm. The Gait: range of motion (RoM) Shank left (L) (degrees) [Mean] might be the most important motor feature for all classification horizons.ConclusionThe symptoms of PD could be objectively quantified. By utilizing suitable motor features to construct ML models, it became possible to intelligently identify whether patients with PD were in the “ON” or “OFF” status. The variations in these motor features were significantly correlated with improvement rates in patients' quality of life. In the future, they might act as objective digital biomarkers to elucidate the changes in symptoms observed in patients with PD and might be used to assist in the diagnosis and treatment of patients with PD

Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy

ObjectiveMesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level.MethodsThis study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy.ResultsWe found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome.SignificanceOur research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy

Thyroid function and epilepsy: a two-sample Mendelian randomization study

BackgroundThyroid hormones (THs) play a crucial role in regulating various biological processes, particularly the normal development and functioning of the central nervous system (CNS). Epilepsy is a prevalent neurological disorder with multiple etiologies. Further in-depth research on the role of thyroid hormones in epilepsy is warranted.MethodsGenome-wide association study (GWAS) data for thyroid function and epilepsy were obtained from the ThyroidOmics Consortium and the International League Against Epilepsy (ILAE) Consortium cohort, respectively. A total of five indicators of thyroid function and ten types of epilepsy were included in the analysis. Two-sample Mendelian randomization (MR) analyses were conducted to investigate potential causal relations between thyroid functions and various epilepsies. Multiple testing correction was performed using Bonferroni correction. Heterogeneity was calculated with the Cochran’s Q statistic test. Horizontal pleiotropy was evaluated by the MR-Egger regression intercept. The sensitivity was also examined by leave-one-out strategy.ResultsThe findings indicated the absence of any causal relationship between abnormalities in thyroid hormone and various types of epilepsy. The study analyzed the odds ratio (OR) between thyroid hormones and various types of epilepsy in five scenarios, including free thyroxine (FT4) on focal epilepsy with hippocampal sclerosis (IVW, OR = 0.9838, p = 0.02223), hyperthyroidism on juvenile absence epilepsy (IVW, OR = 0.9952, p = 0.03777), hypothyroidism on focal epilepsy with hippocampal sclerosis (IVW, OR = 1.0075, p = 0.01951), autoimmune thyroid diseases (AITDs) on generalized epilepsy in all documented cases (weighted mode, OR = 1.0846, p = 0.0346) and on childhood absence epilepsy (IVW, OR = 1.0050, p = 0.04555). After Bonferroni correction, none of the above results showed statistically significant differences.ConclusionThis study indicates that there is no causal relationship between thyroid-related disorders and various types of epilepsy. Future research should aim to avoid potential confounding factors that might impact the study

Bilateral heterochronic spontaneous hemothorax caused by pulmonary arteriovenous malformation in a gravid: A case report

Bilateral heterochronic spontaneous hemothorax as a result of pulmonary ateriovenous malformation is a very rarely happened disease. A 34-year-old woman presented major symptoms with right-sided chest pain and shortness of breath. The following contrast-enhanced computed tomographic scan of the chest showed a large amount of fluid in the right thorax with mediastinal shift, but without major vessel injury and 2 small dense opacities in the apical segment of the right lower lobe and in the posterior aspect of the left lower lobe. The patient underwent local resection of the right lower lobe. The pulmonary ateriovenous malformation was further identified by pathological examination. One month after she was discharged home, the symptoms described above recurred. A follow-up computed tomographic scan of the chest showed a large amount of fluid in the left thorax. During the emergency operation, we found a bullous lesion in the left lower lobe and a small blood vessel overlying the lesion that was actively bleeding. As stated above, local resection of the left lower lobe was performed once more. Pathological result was the same as observed previously. There were no postoperative complications and she was discharged from the hospital after two weeks. Two months later, she successfully delivered a healthy female infant. Up to now, regular follow-up observation has shown her to be perfectly asymptomatic

A bibliometric and knowledge-map analysis of the glymphatic system from 2012 to 2022

ObjectiveTo explore the development context, research hotspots and frontiers in the glymphatic system (GS) field from 2012 to 2022 by bibliometric analysis.MethodsThe Web of Science Core Collection (WoSCC) database was searched for articles published between 2012 and 2022. Microsoft Excel was used to manage the data. VOSviewer, CiteSpace, GraphPad Prism, the Web of Science, and an online analysis platform for bibliometrics (http://bibliometric.com/) were used to analyze the countries, institutions, journals, and collaboration networks among authors and the types of articles, developmental directions, references, and top keywords of published articles.ResultsA total of 412 articles were retrieved, including 39 countries/regions, 223 research institutes and 171 academic journals. The subject classifications related to the GS were Neuroscience, Clinical Neuroscience and Radiology/Nuclear Medicine/Medical Imaging. The United States has maintained its dominant and most influential position in GS research. Among research institutions and journals, the Univ Rochester and Journal of Cerebral Blood Flow and Metabolism had the highest number of academic articles, respectively. Nedergaard M had the most published article, and Iliff JJ had the most co-citations. The top two keywords with the highest frequency were “glymphatic system” and “cerebrospinal fluid.”ConclusionThis research provides valuable information for the study of the GS. The bibliometric analysis of this area will encourage potential collaborations among researchers, defining its frontiers and directions for development

Large-scale prediction of long non-coding RNA functions in a coding–non-coding gene co-expression network

Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding–non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine)

Riboflavin alleviates cardiac failure in Type I diabetic cardiomyopathy

Heart failure (HF) is a common and serious comorbidity of diabetes. Oxidative stress has been associated with the pathogenesis of chronic diabetic complications including cardiomyopathy. The ability of antioxidants to inhibit injury has raised the possibility of new therapeutic treatment for diabetic heart diseases. Riboflavin constitutes an essential nutrient for humans and animals and it is an important food additive. Riboflavin, a precursor of flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD), enhances the oxidative folding and subsequent secretion of proteins. The objective of this study was to investigate the cardioprotective effect of riboflavin in diabetic rats. Diabetes was induced in 30 rats by a single injection of streptozotocin (STZ) (70 mg /kg). Riboflavin (20 mg/kg) was orally administered to animals immediately after induction of diabetes and was continued for eight weeks. Rats were examined for diabetic cardiomyopathy by left ventricular (LV) remadynamic function. Myocardial oxidative stress was assessed by measuring the activity of superoxide dismutase (SOD), the level of malondialdehyde (MDA) as well as heme oxygenase-1 (HO-1) protein level. Myocardial connective tissue growth factor (CTGF) level was measured by Western blot in all rats at the end of the study. In the untreated diabetic rats, left ventricular systolic pressure (LVSP) rate of pressure rose (+dp/dt), and rate of pressure decay (−dp/dt) were depressed while left ventricular end-diastolic pressure (LVEDP) was increased, which indicated the reduced left ventricular contractility and slowing of left ventricular relaxation. The level of SOD decreased, CTGF and HO-1 protein expression and MDA content rose. Riboflavin treatment significantly improved left ventricular systolic and diastolic function in diabetic rats, there were persistent increases in significant activation of SOD and the level of HO-1 protein, and a decrease in the level of CTGF. These results suggest that riboflavin treatment ameliorates myocardial function and improves heart oxidant status, whereas raising myocardial HO-1 and decreasing myocardial CTGF levels have beneficial effects on diabetic cardiomyopathy

Modeling Documents with Event Model

Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks