Accrual of functional redundancy along the lifespan and its effects on cognition.

Despite the necessity to understand how the brain endures the initial stages of age-associated cognitive decline, no brain mechanism has been quantitatively specified to date. The brain may withstand the effects of cognitive aging through redundancy, a design feature in engineered and biological systems, which entails the presence of substitute elements to protect it against failure. Here, we investigated the relationship between functional network redundancy and age over the human lifespan and their interaction with cognition, analyzing resting-state functional MRI images and cognitive measures from 579 subjects. Network-wide redundancy was significantly associated with age, showing a stronger link with age than other major topological measures, presenting a pattern of accumulation followed by old-age decline. Critically, redundancy significantly mediated the association between age and executive function, with lower anti-correlation between age and cognition in subjects with high redundancy. The results suggest that functional redundancy accrues throughout the lifespan, mitigating the effects of age on cognition.

Photoelectrocatalytic Detection and Degradation Studies of a Hazardous Textile Dye Safranin T.

Herein, we report an electrochemical scaffold consisting of functionalized multiwalled carbon nanotubes (COOH-fMWCNTs) and iron-doped zinc oxide nanoparticles (Fe-ZnO) for the detection of a hazardous textile dye safranin T (ST) and monitoring of its photocatalytic degradation. Prior to the detection and degradation analysis, Fe-ZnO NPs were synthesized by the sol-gel method and characterized by a number of structural and morphological techniques. The carboxyl moiety of COOH-fMWCNTs possessing a strong affinity for the amino functionality of ST led to significant enhancement of the current response at the designed electrochemical platform, whereas the electrocatalytic role, surface area enhancement, and the provision of binding sites of Fe-ZnO led to a further increase in the peak current intensity of ST. Electrochemical impedance spectroscopy showed that the sensing scaffold made of the glassy carbon electrode modified with COOH-fMWCNTs and Fe-ZnO efficiently transfers charge between the transducer and the redox probe. Under optimized conditions, the developed sensor showed a 2.3 nM limit of detection for ST. Moreover, recovery experiments and anti-interference tests qualified the sensing platform for practical applications. The dye was photocatalytically degraded using Fe-ZnO NPs up to 99% in 60 min with a rate constant of 0.068 min-1. The designed sensor was used to probe the degradation kinetics of the target dye, and the results were found consistent with the findings obtained from electronic absorption method. To the best of our knowledge, the present work is the first approach for the efficient detection and almost absolute degradation of ST.

Eucalyptus globulus Mediated Green Synthesis of Environmentally Benign Metal Based Nanostructures: A Review.

The progress in nanotechnology has effectively tackled and overcome numerous global issues, including climate change, environmental contamination, and various lethal diseases. The nanostructures being a vital part of nanotechnology have been synthesized employing different physicochemical methods. However, these methods are expensive, polluting, eco-unfriendly, and produce toxic byproducts. Green chemistry having exceptional attributes, such as cost-effectiveness, non-toxicity, higher stability, environment friendliness, ability to control size and shape, and superior performance, has emerged as a promising alternative to address the drawbacks of conventional approaches. Plant extracts are recognized as the best option for the biosynthesis of nanoparticles due to adherence to the environmentally benign route and sustainability agenda 2030 of the United Nations. In recent decades, phytosynthesized nanoparticles have gained much attention for different scientific applications. Eucalyptus globulus (blue gum) is an evergreen plant belonging to the family Myrtaceae, which is the targeted point of this review article. Herein, we mainly focus on the fabrication of nanoparticles, such as zinc oxide, copper oxide, iron oxide, lanthanum oxide, titanium dioxide, magnesium oxide, lead oxide, nickel oxide, gold, silver, and zirconium oxide, by utilizing Eucalyptus globulus extract and its essential oils. This review article aims to provide an overview of the synthesis, characterization results, and biomedical applications of nanoparticles synthesized using Eucalyptus globulus. The present study will be a better contribution to the readers and the students of environmental research.

Model-based stratification of progression along the Alzheimer disease continuum highlights the centrality of biomarker synergies.

BACKGROUND: The progression rates of Alzheimer's disease (AD) are variable and dynamic, yet the mechanisms that contribute to heterogeneity in progression rates remain ill-understood. Particularly, the role of synergies in pathological processes reflected by biomarkers for amyloid-beta ('A'), tau ('T'), and neurodegeneration ('N') in progression along the AD continuum is not fully understood. METHODS: Here, we used a combination of model and data-driven approaches to address this question. Working with a large dataset (N = 321 across the training and testing cohorts), we first applied unsupervised clustering on longitudinal cognitive assessments to divide individuals on the AD continuum into those showing fast vs. moderate decline. Next, we developed a deep learning model that differentiated fast vs. moderate decline using baseline AT(N) biomarkers. RESULTS: Training the model with AT(N) biomarker combination revealed more prognostic utility than any individual biomarkers alone. We additionally found little overlap between the model-driven progression phenotypes and established atrophy-based AD subtypes. Our model showed that the combination of all AT(N) biomarkers had the most prognostic utility in predicting progression along the AD continuum. A comprehensive AT(N) model showed better predictive performance than biomarker pairs (A(N) and T(N)) and individual biomarkers (A, T, or N). CONCLUSIONS: This study combined data and model-driven methods to uncover the role of AT(N) biomarker synergies in the progression of cognitive decline along the AD continuum. The results suggest a synergistic relationship between AT(N) biomarkers in determining this progression, extending previous evidence of A-T synergistic mechanisms.

Lower functional hippocampal redundancy in mild cognitive impairment.

With an increasing prevalence of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in response to an aging population, it is critical to identify and understand neuroprotective mechanisms against cognitive decline. One potential mechanism is redundancy: the existence of duplicate elements within a system that provide alternative functionality in case of failure. As the hippocampus is one of the earliest sites affected by AD pathology, we hypothesized that functional hippocampal redundancy is protective against cognitive decline. We compared hippocampal functional redundancy derived from resting-state functional MRI networks in cognitively normal older adults, with individuals with early and late MCI, as well as the relationship between redundancy and cognition. Posterior hippocampal redundancy was reduced between cognitively normal and MCI groups, plateauing across early and late MCI. Higher hippocampal redundancy was related to better memory performance only for cognitively normal individuals. Critically, functional hippocampal redundancy did not come at the expense of network efficiency. Our results provide support that hippocampal redundancy protects against cognitive decline in aging.

Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-parametric Feature Embedded Siamese Network.

Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.