Notes

Distinct modulation involving tracheal along with laryngopharyngeal hmmm via exceptional laryngeal nerve inside cat.
The modular structure of brain networks supports specialized information processing, complex dynamics, and cost-efficient spatial embedding. Inter-individual variation in modular structure has been linked to differences in performance, disease, and development. There exist many data-driven methods for detecting and comparing modular structure, the most popular of which is modularity maximization. Although modularity maximization is a general framework that can be modified and reparamaterized to address domain-specific research questions, its application to neuroscientific datasets has, thus far, been narrow. Here, we highlight several strategies in which the "out-of-the-box" version of modularity maximization can be extended to address questions specific to neuroscience. First, we present approaches for detecting "space-independent" modules and for applying modularity maximization to signed matrices. Next, we show that the modularity maximization frame is well-suited for detecting task- and condition-specific modules. Finally, we highlight the role of multi-layer models in detecting and tracking modules across time, tasks, subjects, and modalities. In summary, modularity maximization is a flexible and general framework that can be adapted to detect modular structure resulting from a wide range of hypotheses. This article highlights multiple frontiers for future research and applications.Recent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases.Whether it be in a single neuron or a more complex biological system like the human brain, form and function are often directly related. The functional organization of human visual cortex, for instance, is tightly coupled with the underlying anatomy with cortical shape having been shown to be a useful predictor of the retinotopic organization in early visual cortex. Although the current state-of-the-art in predicting retinotopic maps is able to account for gross individual differences, such models are unable to account for any idiosyncratic differences in the structure-function relationship from anatomical information alone due to their initial assumption of a template. Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy in human visual cortex such that more realistic and idiosyncratic maps could be predicted. find more We show that our neural network was not only able to predict the functional organization throughout the visual cortical hierarchy, but that it was also able to predict nuanced variations across individuals. Although we demonstrate its utility for modeling the relationship between structure and function in human visual cortex, our approach is flexible and well-suited for a range of other applications involving data structured in non-Euclidean spaces.Drug labeling informs physicians and patients on the safe and effective use of medication. However, recent studies suggested discrepancies in labeling of the same drug between different regulatory agencies. Here, we evaluated the hepatic safety information in labeling for 549 medications approved by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). Limited discrepancies were found regarding risk for hepatic adverse drug reactions (ADRs) (8.7% in hepatic ADR warnings and 21.3% in contraindication for liver disease), while caution should be exercised over drugs with inconsistencies in contraindications for liver disease and evidence for hepatotoxicity (4.9%). Most discrepancies were attributable to less-severe hepatic events and low-frequency hepatic ADR reports and had limited implication on clinical outcomes.
In addition to gastric sensorimotor dysfunctions, functional dyspepsia (FD) is also variably associated with duodenal micro-inflammation and epithelial barrier dysfunction, the pathogenesis and clinical significance of which are unknown. Our hypothesis was that miRNAs and/or inflammation degrade epithelial barrier proteins, resulting in increased duodenal mucosal permeability in FD.

We compared the duodenal mucosal gene expression and miRNAs invivo permeability (lactulose-mannitol excretion between 0 and 60 and 60 and 120 minutes after saccharide ingestion), exvivo assessments (transmucosal resistance, fluorescein isothiocyanate [FITC]-dextran flux, and basal ion transport), and duodenal histology (light and electron microscopy) in 40 patients with FD and 24 controls.

Compared with controls, the mRNA expression of several barrier proteins (zonula occludens-1, occludin, claudin-12, E-cadherin) was modestly reduced (ie, a fold change of 0.8-0.85) in FD with increased expression of several miRNAs (eg, miR-ulatory miRNAs, and increased small intestinal permeability measured in vivo.
To describe the profile of hospital deaths in Brazil according to the cause of hospitalization during the pre-pandemic (2019) and pandemic period (2020).

Descriptive study based on individual-level records of all hospital admissions with death outcomes reimbursed by the Brazilian National Health System (SUS) in the years 2019 and 2020.

There was a 16.7% increase on the number of hospital deaths in 2020 compared to 2019 (522,686 vs 609,755). COVID-19 was related with 19.5% (118,879) of the total hospital deaths in 2020, surpassing diseases of the circulatory system, 15.4% (93,735), and diseases of the respiratory system, 14.9% (91,035).

COVID-19 was the main cause associated with hospital deaths in Brazilian public hospitals in 2020.
COVID-19 was the main cause associated with hospital deaths in Brazilian public hospitals in 2020.For decades, the pathological definition of the vulnerable plaque led to invaluable insights into the mechanisms that underlie myocardial infarction and stroke. Beyond plaque rupture, other mechanisms, such as erosion, may elicit thrombotic events underlining the complexity and diversity of the atherosclerotic disease. Novel insights, based on single-cell transcriptomics and other "omics" methods, provide tremendous opportunities in the ongoing search for cell-specific determinants that will fine-tune the description of the thrombosis prone lesion. It coincides with an increasing awareness that knowledge on lesion characteristics, cell plasticity and clinical presentation of ischemic cardiovascular events have shifted over the past decades. This shift correlates with an observed changes of cell composition towards phenotypical stabilizing of human plaques. These stabilization features and mechanisms are directly mediated by the cells present in plaques and can be mimicked in vitro via primary plaque cells derived from human atherosclerotic tissues. In addition, the rapidly evolving of sequencing technologies identify many candidate genes and molecular mechanisms that may influence the risk of developing an atherosclerotic thrombotic event - which bring the next challenge in sharp focus how to translate these cell-specific insights into tangible functional and translational discoveries?Proteasome-generated spliced epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry triggered heated debates, which find a representative opinion in one of the two fronts in the recent perspective article by Arie Admon. Briefly, he suggests that proteasomes cannot efficiently catalyse such a reaction, and, thus, that all spliced peptides identified in HLA class I immunopeptidomes and other specimens are artefacts. This hypothesis is in contrast with in vitro, in cellula and in vivo results published since the discovery of proteasome-catalysed peptide splicing in 2004.Vertebrate vision critically depends on an 11-cis-retinoid renewal system known as the visual cycle. At the heart of this metabolic pathway is an enzyme known as retinal pigment epithelium 65 kDa protein (RPE65), which catalyzes an unusual, possibly biochemically unique, reaction consisting of a coupled all-trans-retinyl ester hydrolysis and alkene geometric isomerization to produce 11-cis-retinol. Early work on this isomerohydrolase demonstrated its membership to the carotenoid cleavage dioxygenase superfamily and its essentiality for 11-cis-retinal production in the vertebrate retina. Three independent studies published in 2005 established RPE65 as the actual isomerohydrolase instead of a retinoid-binding protein as previously believed. Since the last devoted review of RPE65 enzymology appeared in this journal, major advances have been made in a number of areas including our understanding of the mechanistic details of RPE65 isomerohydrolase activity, its phylogenetic origins, the relationship of its membrane binding affinity to its catalytic activity, its role in visual chromophore production for rods and cones, its modulation by macromolecules and small molecules, and the involvement of RPE65 mutations in the development of retinal diseases. In this article, I will review these areas of progress with the goal of integrating results from the varied experimental approaches to provide a comprehensive picture of RPE65 biochemistry. Key outstanding questions that may prove to be fruitful future research pursuits will also be highlighted.
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