Effects of a Low-Protein Diet on Kidney Function in Patients With Chronic Kidney Disease: An Umbrella Review of Systematic Reviews and Meta-analyses of Randomized Controlled Trials.

Context: Chronic kidney disease (CKD) stands out as one of the most widespread diseases globally. Dietary interventions, such as adopting a low-protein diet (LPD), play a crucial role as a key approach in impeding the advancement of CKD.

Objective: The objective of this umbrella review was to provide understanding into the effects of an LPD on kidney function among individuals with CKD, along with evaluating the certainty of the available evidence.

Effect of stevia on blood glucose and HbA1C: A meta-analysis.

Background: The study investigates substituting non-nutritive sweeteners (NNS) for sugar to address health concerns related to excess sugar intake. It specifically examines how stevia affects insulin and blood glucose levels. The systematic review and meta-analysis aim to evaluate stevia's impact on glycemic indices.

Quasicriticality explains variability of human neural dynamics across life span.

Aging impacts the brain's structural and functional organization and over time leads to various disorders, such as Alzheimer's disease and cognitive impairment. The process also impacts sensory function, bringing about a general slowing in various perceptual and cognitive functions. Here, we analyze the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) resting-state magnetoencephalography (MEG) dataset-the largest aging cohort available-in light of the quasicriticality framework, a novel organizing principle for brain functionality which relates information processing and scaling properties of brain activity to brain connectivity and stimulus.

Spike-phase coupling as an order parameter in a leaky integrate-and-fire model.

It is known that the leaky integrate-and-fire neural model shows a transition from irregular to synchronous firing by increasing the coupling between the neurons. However, a quantitative characterization of this order-disorder transition, that is, the determination of the order of transition and also the critical exponents in the case of continuous transition, is not entirely known. In this work, we consider a network of N excitatory neurons with local connections, residing on a square lattice with periodic boundary conditions.

Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) Found by Hierarchical Clustering.

A hierarchical clustering algorithm was applied to magnetic resonance images (MRI) of a cohort of 751 subjects having a mild cognitive impairment (MCI), 282 subjects having received Alzheimer's disease (AD) diagnosis, and 428 normal controls (NC). MRIs were preprocessed to gray matter density maps and registered to a stereotactic space. By first rendering the gray matter density maps comparable by regressing out age, gender, and years of education, and then performing the hierarchical clustering, we found clusters displaying structural features of typical AD, cortically-driven atypical AD, limbic-predominant AD, and early-onset AD (EOAD).

Thermosensitive liposomal gels loaded with antimicrobial agent for oral care in critically ill patients.

A novel thermosensitive gel loaded with meropenem (MP) liposomes was designed to improve retention in the oral cavity as a prophylactic measure to prevent ventilator-acquired pneumonia in critically ill patients. Meropenem liposomes were incorporated into poloxamer 407 gels and gamma irradiated. Mean size of liposome was 247 nm, polydispersity index < 0.

Stability of neuronal avalanches and long-range temporal correlations during the first year of life in human infants.

During infancy, the human brain rapidly expands in size and complexity as neural networks mature and new information is incorporated at an accelerating pace. Recently, it was shown that single-electrode EEG in preterms at birth exhibits scale-invariant intermittent bursts. Yet, it is currently not known whether the normal infant brain, in particular, the cortex, maintains a distinct dynamical state during development that is characterized by scale-invariant spatial as well as temporal aspects.

Retraction Note to: Stability of neuronal avalanches and long-range temporal correlations during the first year of life in human infant.

The authors have retracted this article Jannesari et al. (2019) because an incorrect version of the article was published in error. The manuscript has been republished as Jannesari et al.

Stability of neuronal avalanches and long-range temporal correlations during the first year of life in human infant.

During infancy, the human brain rapidly expands in size and complexity as neural networks mature and new information is incorporated at an accelerating pace. Recently, it was shown that single electrode EEG in preterms at birth exhibits scale-invariant intermittent bursts. Yet, it is currently not known whether the normal infant brain, in particular, the cortex maintains a distinct dynamical state during development that is characterized by scale-invariant spatial as well as temporal aspects.

Role-play versus lecture methods in community health volunteers.

Background: Considering the key role of health volunteers in promoting community's health, their effective training is of particular importance. Training can be more effective through cooperative and learner-centered methods. Role-play is among the cooperative methods with numerous advantages.

The Conference of the Birds: An Old Artistic Concept Making Sense in Modern Sciences.

In this article, we will discuss scientific aspects of an old Persian story, Simorgh, in the book of . The story is fulfilled with artistic and philosophical metaphors that make sense in two hot topics of the contemporary modern sciences i.e.

Neuronal avalanches: Where temporal complexity and criticality meet.

The model of the current paper is an extension of a previous publication, wherein we have used the leaky integrate-and-fire model on a regular lattice with periodic boundary conditions, and introduced the temporal complexity as a genuine signature of criticality. In that work, the power-law distribution of neural avalanches was a manifestation of supercriticality rather than criticality. Here, however, we show that the continuous solution of the model and replacing the stochastic noise with a Gaussian zero-mean noise leads to the coincidence of power-law display of temporal complexity, and spatiotemporal patterns of neural avalanches at the critical point.

New Perspectives on Spontaneous Brain Activity: Dynamic Networks and Energy Matter.

Spontaneous brain activity has received increasing attention as demonstrated by the exponential rise in the number of published article on this topic over the last 30 years. Such "intrinsic" brain activity, generated in the absence of an explicit task, is frequently associated with resting-state or default-mode networks (DMN)s. The focus on characterizing spontaneous brain activity promises to shed new light on questions concerning the structural and functional architecture of the brain and how they are related to "mind".

Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine.

Objectives: This study assesses the ability of a novel, "automatic classification" approach to facilitate identification of infants at highest familial risk for language-learning disorders (LLD) and to provide converging assessments to enable earlier detection of developmental disorders that disrupt language acquisition.

Methods: Network connectivity measures derived from 62-channel electroencephalogram (EEG) recording were used to identify selected features within two infant groups who differed on LLD risk: infants with a family history of LLD (FH+) and typically-developing infants without such a history (FH-). A support vector machine was deployed; global efficiency and global and local clustering coefficients were computed.

Cooperation in neural systems: bridging complexity and periodicity.

Inverse power law distributions are generally interpreted as a manifestation of complexity, and waiting time distributions with power index μ<2 reflect the occurrence of ergodicity-breaking renewal events. In this paper we show how to combine these properties with the apparently foreign clocklike nature of biological processes. We use a two-dimensional regular network of leaky integrate-and-fire neurons, each of which is linked to its four nearest neighbors, to show that both complexity and periodicity are generated by locality breakdown: Links of increasing strength have the effect of turning local interactions into long-range interactions, thereby generating time complexity followed by time periodicity.