Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study.

BACKGROUND: Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method. OBJECTIVE: This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices. METHODS: This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant's hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment-screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22). RESULTS: We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data. CONCLUSIONS: The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants' cognitive impairment compared to conventional paper-based feature assessment.

Transcriptome Analysis of Salt-Sensitive and Tolerant Genotypes Reveals Salt-Tolerance Metabolic Pathways in Sugar Beet.

Soil salinization is a common environmental problem that seriously affects the yield and quality of crops. Sugar beet (Beta vulgaris L.), one of the main sugar crops in the world, shows a strong tolerance to salt stress. To decipher the molecular mechanism of sugar beet under salt stress, we conducted transcriptomic analyses of two contrasting sugar beet genotypes. To the best of our knowledge, this is the first comparison of salt-response transcriptomes in sugar beet with contrasting genotypes. Compared to the salt-sensitive cultivar (S710), the salt-tolerant one (T710MU) showed better growth and exhibited a higher chlorophyll content, higher antioxidant enzyme activity, and increased levels of osmotic adjustment molecules. Based on a high-throughput experimental system, 1714 differentially expressed genes were identified in the leaves of the salt-sensitive genotype, and 2912 in the salt-tolerant one. Many of the differentially expressed genes were involved in stress and defense responses, metabolic processes, signal transduction, transport processes, and cell wall synthesis. Moreover, expression patterns of several genes differed between the two cultivars in response to salt stress, and several key pathways involved in determining the salt tolerance of sugar beet, were identified. Our results revealed the mechanism of salt tolerance in sugar beet and provided potential metabolic pathways and gene markers for growing salt-tolerant cultivars.

Pseudomonas aeruginosa infection alters the macrophage phenotype switching process during wound healing in diabetic mice.

Macrophages play critical roles in wound healing process. They switch from "classically activated" (M1) phenotype in the early inflammatory phase to "alternatively activated" (M2) phenotype in the later healing phase. However, the dynamic process of macrophage phenotype switching in diabetic wounds burdened with bacteria is unclear. In this report, Pseudomonas aeruginosa, frequently detected in diabetic foot ulcers, was inoculated into cutaneous wounds of db/db diabetic mice to mimic bacterium-infected diabetic wound healing. We observed that P. aeruginosa infection impaired diabetic wound healing and quickly promoted the expression of pro-inflammatory genes (M1 macrophage markers) tumor necrosis factor-α (tnf-α), interleukin-1ß (il-1ß) and il-6 in wounds. The expression of markers of M2 macrophages, including il-10, arginase-1, and ym1 were also upregulated. In addition, similar gene expression patterns were observed in macrophages isolated directly from wounds. Immunostaining showed that P. aeruginosa infection increased both the ratios of M1 and M2 macrophages in wounds compared with that in control groups, which was further confirmed by in vitro culturing macrophages with P. aeruginosa and skin fibroblast conditioned medium. However, the ratios of the expression levels of pro-inflammatory genes to anti-inflammatory gene il-10 was increased markedly in P. aeruginosa infected wounds and macrophages compared with that in control groups, and P. aeruginosa prolonged the presence of M1 macrophages in the wounds. These data demonstrated that P. aeruginosa in diabetic wounds activates a mixed M1/M2 macrophage phenotype with an excessive activation of M1 phenotype or relatively inadequate activation of M2 phenotype.

Correlation of Brain Perfusion with White Matter Hyperintensity, Brain Atrophy, and Cognition in Patients with Posterior Cerebral Artery Stenosis and Subjective Cognitive Decline.

BACKGROUND This study aimed to investigate the correlation of brain perfusion with white matter hyperintensity (WMH), brain atrophy, and cognition in patients with moderate to severe posterior cerebral artery stenosis (PCAS). MATERIAL AND METHODS 65 patients with memory decline as the main complaint and no history of brain infarction were recruited from the Department of Neurology of Tongji Hospital. Patients with moderate to severe PCAS were included in case group, and subjects with normal intracranial blood vessels served as controls. The demographics and vascular risk factors were recorded. Montreal Cognitive Assessment (MoCA) was used to evaluate the cognition. CT perfusion imaging was performed, and WASID was employed for the assessment of intracranial artery stenosis. The region of interest (ROI) was analyzed based on the whole brain perfusion. Cranial MRI was performed, and Scheltens scoring system was used for the assessment of WMH on FLAIR. T1 weighed images were obtained, and global cortical atrophy (GCA) scale was employed for the assessment of brain atrophy. The detections of brain perfusion, WMH and brain atrophy were done at centrum ovale, parietal lateral ventricle and basal ganglia layers. RESULTS In PCAS patients we found low perfusion in the antecornu and postcornu blood supply areas at the lateral ventricle, the blood supply area of the anterior cerebral artery, the blood supply area of the posterior cerebral artery, and the blood supply area at the hippocampus as compared with control subjects (p<0.05). As compared with control subjects, the incidence of WMH in the blood supply areas at the deep brain and lateral ventricle was significantly higher in PCAS patients (p<0.05). When compared with controls, the incidence of brain atrophy increased significantly in PCAS patients (p<0.01). Correlation analysis showed the brain perfusion at the blood supply area of the posterior cerebral artery was positively correlated to the total MoCA score and negatively correlated to the severity of WMH at the blood supply area of the posterior cerebral artery (p<0.05). Further analysis showed the brain perfusion at the blood supply area of the posterior cerebral artery was negatively associated with cortex supplied by the posterior cerebral artery, posterior cingulate, and hippocampus (p<0.01). CONCLUSIONS PCAS patients have a higher incidence of brain atrophy, and the perfusion at the area supplied by the posterior cerebral artery is correlated to the severity of brain atrophy and of WMH, as well as to cognition decline.

Precise prediction of cerebrospinal fluid amyloid beta protein for early Alzheimer's disease detection using multimodal data.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder marked by a progressive decline in cognitive function and memory capacity. The accurate diagnosis of this condition predominantly relies on cerebrospinal fluid (CSF) markers, notwithstanding the associated burdens of pain and substantial financial costs endured by patients. This study encompasses subjects exhibiting varying degrees of cognitive impairment, encompassing individuals with subjective cognitive decline, mild cognitive impairment, and dementia, constituting a total sample size of 82 participants. The primary objective of this investigation is to explore the relationships among brain atrophy measurements derived from magnetic resonance imaging, atypical electroencephalography (EEG) patterns, behavioral assessment scales, and amyloid ß-protein (Aß) indicators. The findings of this research reveal that individuals displaying reduced Aß1-42/Aß-40 levels exhibit significant atrophy in the frontotemporal lobe, alongside irregularities in various parameters related to EEG frequency characteristics, signal complexity, inter-regional information exchange, and microstates. The study additionally endeavors to estimate Aß1-42/Aß-40 content through the application of a random forest algorithm, amalgamating structural data, electrophysiological features, and clinical scales, achieving a remarkable predictive precision of 91.6%. In summary, this study proposes a cost-effective methodology for acquiring CSF markers, thereby offering a valuable tool for the early detection of AD.

Fabrication and evaluation of silver modified micro/nano structured titanium implant.

In order to enhance the antibacterial property of titanium implant without inducing obvious cytotoxicity, the combination of Ag nanolayer and micro/nano surface structure was conducted by magnetron sputtering and hot-alkali treatment in this study. A series of specimens (AH-Ti, AH-Ti/Ag0.25, AH-Ti/Ag1, AH-Ti/Ag2, and AH-Ti/Ag5) were prepared with different sputtering durations (0 min, 0.25 min, 1 min, 2 min, 5 min), respectively, all realizing long-term release of Ag+. In vitro experiments indicated that AH-Ti/Ag1 group possessed good cytocompatibility, nice osteogenic ability, and excellent antibacterial efficiency as well. In addition, AH-Ti/Ag0.25 showed good biocompatibility, while the reduction of S.aureus (78.5%) was not enough compared with AH-Ti/Ag1. Although the AH-Ti/Ag2 and AH-Ti/Ag5 group showed superior antibacterial activity, their obvious cytotoxicity caused low ALP and mineralization level. Therefore, the design of suitable Ag nanolayer coating combined with micro/nano surface structure (AH-Ti/Ag1) might be a promising strategy to enhance osteogenic property and maintain excellent antibacterial ability at the same time.

S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test.

BACKGROUND: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. METHODOLOGY: In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. RESULTS: The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. CONCLUSIONS: In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff.

Multiple cross-frequency coupling analysis of resting-state EEG in patients with mild cognitive impairment and Alzheimer's disease.

Introduction: Electroencephalographic (EEG) abnormalities are seen in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) with characteristic features of cognitive impairment. The most common findings of EEG features in AD and MCI patients are increased relative power of slow oscillations (delta and theta rhythms) and decreased relative power of fast oscillations (alpha, beta and gamma rhythms). However, impairments in cognitive processes in AD and MCI are not sufficiently reflected by brain oscillatory activity in a particular frequency band. MCI patients are at high risk of progressing to AD. Cross-frequency coupling (CFC), which refers to coupling between different frequency bands, is a crucial tool for comprehending changes in brain oscillations and cognitive performance. CFC features exhibit some specificity in patients with AD and MCI, but a comparison between CFC features in individuals with these disorders is still lacking. The aim of this study was to explore changes in CFC properties in MCI and AD and to explore the relationship between CFC properties and multiple types of cognitive functional performance. Methods: We recorded resting-state EEG (rsEEG) signals in 46 MCI patients, 43 AD patients, and 43 cognitively healthy controls (HCs) and analyzed the changes in CFC as well as the relationship between CFC and scores on clinical tests of cognitive function. Results and discussion: Multiple couplings between low-frequency oscillations and high-frequency oscillations were found to be significantly enhanced in AD patients compared to those of HCs and MCI, while delta-gamma as well as theta-gamma couplings in the right temporal and parietal lobes were significantly enhanced in MCI patients compared to HCs. Moreover, theta-gamma coupling in the right temporal lobe tended to be stronger in MCI patients than in HCs, and it was stronger in AD than in MCI. Multiple CFC properties were found to correlate significantly with various cognitive domains, especially the memory function domain. Overall, these findings suggest that AD and MCI patients must use more neural resources to maintain a resting brain state and that alterations in theta-gamma coupling in the temporal lobe become progressively obvious during disease progression and are likely to be a valuable indicator of MCI and AD pathology.

Silver-catalyzed chemodivergent assembly of aminomethylated isochromenes and naphthols.

A silver-catalyzed chemodivergent cyclization of alkyne-tethered aldehydes with aminals to aminomethylated 1H-isochromenes and naphthols is described by tuning the reaction conditions. The reaction exhibits broad substrate generality and functional group compatibility. Mechanistic studies have disclosed that the aminomethylated naphthols are generated from the resulting N,O-aminal containing isochromenes via a silver-catalyzed unusual rearrangement process.

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.

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.

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.

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.

Palladium-Catalyzed Aminomethylative Oppolzer-Type Cyclization of Enynes: Access to Aminomethylated Benzofulvenes.

A novel palladium-catalyzed Oppolzer-type cyclization reaction aided by the aminomethyl cyclopalladated complex has been developed, which provides rapid access to functionalized benzofulvenes with excellent stereoselectivity. The corresponding products can undergo Diels-Alder reaction with maleimides, providing a series of complex polycyclic compounds with excellent regio- and stereoselectivities.

MRI study of cerebral blood flow, vascular reactivity, and vascular coupling in systemic hypertension.

Chronic hypertension alters cerebrovascular function, which can lead to neurovascular pathologies and increased susceptibility to neurological disorders. The purpose of this study was to utilize in vivo MRI methods with corroborating immunohistology to evaluate neurovascular dysfunction due to progressive chronic hypertension. The spontaneously hypertensive rat (SHR) model at different stages of hypertension was studied to evaluate: i) basal cerebral blood flow (CBF), ii) cerebrovascular reactivity (CVR) assessed by CBF and blood-oxygenation level dependent (BOLD) signal changes to hypercapnia, iii) neurovascular coupling from CBF and BOLD changes to forepaw stimulation, and iv) damage of neurovascular unit (NVU) components (microvascular, astrocyte and neuron densities). Comparisons were made with age-matched normotensive Wistar Kyoto (WKY) rats. In 10-week SHR (mild hypertension), basal CBF was higher (p < 0.05), CVR trended higher, and neurovascular coupling response was higher (p < 0.05), compared to normotensive rats. In 40-week SHR (severe hypertension), basal CBF, CVR, and neurovascular coupling response were reversed to similar or below normotensive rats, and were significantly different from 10-week SHR (p < 0.05). Immunohistological analysis found significantly reduced microvascular density, increased astrocytes, and reduced neuronal density in SHR at 40 weeks (p < 0.05) but not at 10 weeks (p > 0.05) in comparison to age-matched controls. In conclusion, we observed a bi-phasic basal CBF, CVR and neurovascular coupling response from early to late hypertension using in vivo MRI, with significant changes prior to changes in the NVU components from histology. MRI provides clinically relevant data that might be useful to characterize neurovascular pathogenesis on the brain in hypertension.

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.

The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (-3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

Extended Application of Digital Clock Drawing Test in the Evaluation of Alzheimer's Disease Based on Artificial Intelligence and the Neural Basis.

INTRODUCTION: This study aimed to build the supervised learning model to predict the state of cognitive impairment, Alzheimer's Disease (AD) and cognitive domains including memory, language, action, and visuospatial based on Digital Clock Drawing Test (dCDT) precisely. METHODS: 207 normal controls, 242 Mild Cognitive Impairment (MCI) patients, 87 dementia patients, including 53 AD patients, were selected from Shanghai Tongji Hospital. The electromagnetic tablets were used to collect the trajectory points of dCDT. By combining dynamic process and static results, different types of features were extracted, and the prediction models were built based on the feature selection approaches and machine learning methods. RESULTS AND DISCUSSION: The optimal AUC of cognitive impairment's screening, AD's screening and differentiation are 0.782, 0.919 and 0.818, respectively. In addition, the cognitive state of the domains with the best prediction result based on the features of dCDT is action with the optimal AUC 0.794, while the other three cognitive domains got the prediction results between 0.744-0.755. CONCLUSION: By extracting dCDT features, cognitive impairment and AD patients can be identified early. Through dCDT feature extraction, a prediction model of single cognitive domain damage can be established.

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.

Physiological and Transcriptome Analysis of Sugar Beet Reveals Different Mechanisms of Response to Neutral Salt and Alkaline Salt Stresses.

The salinization and alkalization of soil are widespread environmental problems. Sugar beet (B. vulgaris L.) is a moderately salt tolerant glycophyte, but little is known about the different mechanisms of sugar beet response to salt and alkaline stresses. The aim of this study was to investigate the influence of neutral salt (NaCl:Na2SO4, 1:1) and alkaline salt (Na2CO3) treatment on physiological and transcriptome changes in sugar beet. We found that a low level of neutral salt (NaCl:Na2SO4; 1:1, Na+ 25 mM) or alkaline salt (Na2CO3, Na+ 25 mM) significantly enhanced total biomass, leaf area and photosynthesis indictors in sugar beet. Under a high concentration of alkaline salt (Na2CO3, Na+ 100 mM), the growth of plants was not significantly affected compared with the control. But a high level of neutral salt (NaCl: Na2SO4; 1:1, Na+ 100 mM) significantly inhibited plant growth and photosynthesis. Furthermore, sugar beet tends to synthesize higher levels of soluble sugar and reducing sugar to cope with high neutral salt stress, and more drastic changes in indole acetic acid (IAA) and abscisic acid (ABA) contents were detected. We used next-generation RNA-Seq technique to analyze transcriptional changes under neutral salt and alkaline salt treatment in sugar beet. Overall, 4,773 and 2,251 differentially expressed genes (DEGs) were identified in leaves and roots, respectively. Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that genes involving cutin, suberine and wax biosynthesis, sesquiterpenoid and triterpenoid biosynthesis and flavonoid biosynthesis had simultaneously changed expression under low neutral salt or alkaline salt, so these genes may be related to stimulating sugar beet growth in both low salt treatments. Genes enriched in monoterpenoid biosynthesis, amino acids metabolism and starch and sucrose metabolism were specifically regulated to respond to the high alkaline salt. Meanwhile, compared with high alkaline salt, high neutral salt induced the expression change of genes involved in DNA replication, and decreased the expression of genes participating in cutin, suberine and wax biosynthesis, and linoleic acid metabolism. These results indicate the presence of different mechanisms responsible for sugar beet responses to neutral salt and alkaline salt stresses.

Effect of Methylene Blue and PI3K-Akt Pathway Inhibitors on the Neurovascular System after Chronic Cerebral Hypoperfusion in Rats.

Methylene blue (MB) has a protective effect on cognitive decline caused by chronic hypoperfusion, but the specific mechanism is not clear. This article aims to determine whether MB protects vascular neurons through PI3K/Akt and plays a role in improving cognitive impairment. Molecular biological methods, the hippocampal neuronal density test, the hippocampal vascular network density test, and dynamic enhanced magnetic resonance imaging (MRI) were used to detect the blood-brain barrier permeability and Evans blue leakage rate in the hippocampus. We also observed and evaluated the changes in the above results after administration of the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) pathway protein inhibitor LY294002. There were significant differences for cerebral blood flow (CBF) between the chronic cerebral hypoperfusion (CCH) + MB group (100 ml/100 g/min) and the CCH group (60 ml/100 g/min, P < 0.05). After using LY294002, the CBF of the CCH + MB + LY294002 group dropped to 82 ml/100 g/min. The vascular density in the CCH + MB group was 23%, which is significantly higher than that in the CCH group (15.1%) (P < 0.05). The vascular density (17.5%) in the CCH + MB + LY294002 group was significantly higher than that in the CCH group but lower than that in the CCH + MB group. Western blotting results showed that one week after intraperitoneal injection of MB, the expression of t-Akt and p-Akt in the CCH + MB group was increased after CCH, and LY294002 partially blocked this up-regulation effect (CCH + MB + LY294002 group). MB is a potential therapy for the relief of mild cognitive impairment associated with CCH, vascular dementia, and Alzheimer's disease.