Hollow glass microspheres embedded in porous network of chitosan aerogel used for thermal insulation and flame retardant materials.

In recent years, biopolymer aerogels as thermal insulation materials have received widespread attention due to natural abundance, cost-efficiency, and environment-friendly. However, the flammability and low strength hinder its practical application. Hollow glass microspheres (HGMs) as an inorganic thermal insulation filler have been filled in biopolymer aerogels to improve flame retardancy. However, the structure formed by HGMs embedded porous network of biopolymer aerogel has rarely been investigated, which not only reduce thermal conductivity through high porosity, but also adjust the filling volume of HGMs and achieve uniform distribution through chemical cross-linking. Herein, a biopolymer aerogel composite was assembled by chitosan aerogel (CSA) and different volume of HGMs by chemical cross-linking, freeze-drying, and silylation modification processes. When the filling volume fraction of HGMs reached 40 %, a skeleton structure was initially formed. The composites with HGMs volume of 40 %-60 % exhibited low density, high porosity, low thermal conductivity, good mechanical property, and excellent flame retardancy. According to GB 8624-2012 standard for classification, the composite with 60 % HGMs achieved class A1 non-combustible.

Cu-Co Hybrid Crystals Assembled on Hollow Microsphere: Temperature-Dependent Top-Down Synthesis and Aggregation-Induced Conversion from Microwave Shielding to Absorption.

The construction of hollow metallic microspheres with rationally designed building blocks of the metal shell is a promising way to achieve low density and functionality control, but the microengineering of the metallic structures on a micrometer spherical surface is a great challenge. In the present work, a novel and simple calcination-induced aggregation strategy is developed to realize the distribution status and microstructure control of Co-Cu bimetal building blocks assembled on a hollow glass microsphere support, and thus a series of low-density (0.58 g cm-3 ) dual shell composite hollow microspheres are constructed with gradient in electromagnetic property depending on the calcination temperature (CT). The optimized microwave shielding performance can be achieved at a CT of 500 °C, while further increasing CT to 700 °C leads to an interesting conversion from microwave shielding to absorption with an optimized effective absorption bandwidth of 4.64 GHz at a low matching thickness of 1.33 mm. The mechanism underlying the CT-dependent metallic shell structure variation and further the decisive effect of the shell structure on the microwave response behavior are proposed based on a series of contrast experiments.

Comparative analysis of sensitivity and specificity of computer-aided cognitive test in screening mild cognitive impairment patients and test of reliability and validity.

OBJECTIVES: To evaluate the reliability and validity of the computer-aided cognitive test (CACT). METHODS: 219 Subjects of Tongji Hospital's Brain Health cohort (115 cases of Mild Cognitive Impairment (MCI) patients and 104 cases of normal controls) were enrolled, of which 24 cases received a retest after 2 weeks. Finally, the reliability and validity of the scale were tested and analyzed. RESULTS: (1) Reliability: (a) the internal consistency reliability of the total score of the scale was 0.645; (b) the retest reliability correlation coefficient of the total score of the scale was 0.900; (c) the Guttman Split-Half coefficient was 0.631; (2) Validity: (a) construct validity analysis showed that the correlation coefficient between each section score was between 0.036 and 0.408, and the correlation coefficient between each section score and the total score was between 0.468 and 0.781; (b) criterion validity analysis showed that the correlation coefficient between the total score of CACT and that of the Mini Mental State Examination (MMSE) was 0.733, and the coefficient between the total score of CACT and that of the basic version of the Montreal Cognitive Assessment (MoCA) was 0.763; (c) the area under the ROC curve of the CACT to distinguish between MCI patients and controls was 0.920, with an optimal diagnostic threshold of 20, a sensitivity of 88.5%, and a specificity of 80.9%. CONCLUSION: The CACT is little influenced by education level. It has good reliability and validity, which can be used for early clinical screening of cognitive dysfunction.

Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics.

BACKGROUND: Brain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable. METHOD: We acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains. RESULTS: The extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu. CONCLUSION: For nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder.

Dysfunction of Emotion Regulation in Mild Cognitive Impairment Individuals Combined With Depressive Disorder: A Neural Mechanism Study.

Depression increases the risk of progression from mild cognitive impairment (MCI) to dementia, where impaired emotion regulation is a core symptom of depression. However, the neural mechanisms underlying the decreased emotion regulation in individuals with MCI combined with depressive symptoms are not precise. We assessed the behavioral performance by emotion regulation tasks and recorded event-related electroencephalography (EEG) signals related to emotion regulation tasks simultaneously. EEG analysis, including event-related potential (ERP), event-related spectral perturbation (ERSP), functional connectivity and graph theory, was used to compare the difference between MCI individuals and MCI depressed individuals in behavioral performance, the late positive potential (LPP) amplitudes, neural oscillations and brain networks during the processing of emotional stimuli. We found that MCI depressed individuals have negative preferences and are prone to allocate more attentional resources to negative stimuli. Results suggested that theta and alpha oscillations activity is increased, and gamma oscillations activity is decreased during negative stimulus processing in MCI depressed individuals, thus indicating that the decreased emotion regulation in MCI depressed individuals may be associated with enhanced low-frequency and decreased high-frequency oscillations activity. Functional connectivity analysis revealed a decrease in functional connectivity in the left cerebral hemisphere of the alpha band and an increase in functional connectivity in the right cerebral hemisphere of the alpha band in MCI depressed individuals. Graph theory analysis suggested that global network metrics, including clustering coefficients and disassortative, decreased, while nodal and modular network metrics regarding local nodal efficiency, degree centrality, and betweenness centrality were significantly increased in the frontal lobe and decreased in the parieto-occipital lobe, which was observed in the alpha band, further suggesting that abnormal alpha band network connectivity may be a potential marker of depressive symptoms. Correlational analyses showed that depressive symptoms were closely related to emotion regulation, power oscillations and functional connectivity. In conclusion, the dominant processing of negative stimuli, the increased low-frequency oscillations activity and decreased high-frequency activity, so as the decrease in top-down information processing in the frontal parieto-occipital lobe, results in the abnormality of alpha-band network connectivity. It is suggested that these factors, in turn, contribute to the declined ability of MCI depressed individuals in emotion regulation.

Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer's Disease and Mild Cognitive Impairment.

Objective: Alzheimer's Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD. Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests. Results: 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment. Conclusion: Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.

Neural Mechanism of Repeated Transcranial Magnetic Stimulation to Enhance Visual Working Memory in Elderly Individuals With Subjective Cognitive Decline.

Visual working memory (VWM), the core process inherent to many advanced cognitive processes, deteriorates with age. Elderly individuals usually experience defects in the processing of VWM. The dorsolateral prefrontal cortex is a key structure for the top-down control of working memory processes. Many studies have shown that repeated transcranial magnetic stimulation (rTMS) improves VWM by modulating the excitability of neurons in the target cortical region, though the underlying neural mechanism has not been clarified. Therefore, this study sought to assess the characteristics of brain memory function post-rTMS targeting the left dorsolateral prefrontal cortex. The study stimulated the left dorsolateral prefrontal cortex in elderly individuals by performing a high-frequency rTMS protocol and evaluated behavioral performance using cognitive tasks and a VWM task. Based on the simultaneously recorded electroencephalogram signals, event-related potential and event-related spectral perturbation analysis techniques were used to investigate the variation characteristics of event-related potential components' (N2PC and CDA) amplitudes and neural oscillations in elderly individuals to elucidate the effect of high-frequency rTMS. The results found that rTMS enhanced VWM performance and significantly improved attention and executive function in elderly individuals with subjective cognitive decline. We therefore speculate that rTMS enhances VWM by increasing the N2PC and CDA amplitude, alongside increasing ß oscillation activity. This would improve the attention and allocation of resources in elderly individuals such as to improve an individual's VWM.