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Evaluation of reactive astrogliosis by neuroanatomical assays represents a common experimental outcome for neuroanatomists. The literature demonstrates several conflicting results as to the accuracy of such measures. We posited that the diverging results within the neuroanatomy literature were due to suboptimal analytical workflows in addition to astrocyte regional heterogeneity. We therefore generated an automated segmentation workflow to extract features of glial fibrillary acidic protein (GFAP) and aldehyde dehydrogenase family 1, member L1 (ALDH1L1) labeled astrocytes with and without neuroinflammation. We achieved this by capturing multiplexed immunofluorescent confocal images of mouse brains treated with either vehicle or lipopolysaccharide (LPS) followed by implementation of our workflows. Using classical image analysis techniques focused on pixel intensity only, we were unable to identify differences between vehicle‐treated and LPS‐treated animals. However, when utilizing machine learning–based algorithms, we were able to (1) accurately predict which objects were derived from GFAP or ALDH1L1‐stained images indicating that GFAP and ALDH1L1 highlight distinct morphological aspects of astrocytes, (2) we could predict which neuroanatomical region the segmented GFAP or ALDH1L1 object had been derived from, indicating that morphological features of astrocytes change as a function of neuroanatomical location. (3) We discovered a statistically significant, albeit not highly accurate, prediction of which objects had come from LPS versus vehicle‐treated animals, indicating that although features exist capable of distinguishing LPS‐treated versus vehicle‐treated GFAP and ALDH1L1‐segmented objects, that significant overlap between morphologies exists. We further determined that for most classification scenarios, nonlinear models were required for improved treatment class designations. We propose that unbiased automated image analysis techniques coupled with well‐validated machine learning tools represent highly useful models capable of providing insights into neuroanatomical assays. [ABSTRACT FROM AUTHOR]

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availability of open-source programming languages, and the objectivity permitted in their data analysis. We used classification-based algorithms, including random forest, gradient boosted machines, support vector machines, and neural networks, to test the hypothesis that the animal genotypes could be separated into their genotype based on interpretation of neurophysiological recordings. We then interrogate the models to identify what were the major features utilized by the algorithms to designate genotype classification. By using raw EEG and respiratory plethysmography data, we were able to predict which recordings came from genotype class with accuracies that were significantly improved relative to the no information rate, although EEG analyses showed more overlap between groups than respiratory plethysmography. In comparison, conventional methods where single features between animal classes were analyzed, differences between the genotypes tested using baseline neurophysiology measurements showed no statistical difference. However, ML/AI workflows successfully were capable of providing successful classification, indicating that interactions between features were different in these genotypes. ML/AI workflows provide new methodologies to interrogate neurophysiology data. However, their implementation must be done with care so as to provide high rigor and reproducibility between laboratories. We provide a series of recommendations on how to report the utilization of ML/AI workflows for the neurophysiology community. [ABSTRACT FROM AUTHOR]

Congenital central hypoventilation syndrome (CCHS) represents a rare genetic disorder usually caused by mutations in the homeodomain transcription factor PHOX2B. Some CCHS patients suffer mainly from deficiencies in CO2 and/or O2 respiratory chemoreflex, whereas other patients present with full apnea shortly after birth. Our goal was to identify the neuropathological mechanisms of apneic presentations in CCHS. In the developing murine neuroepithelium, Phox2b is expressed in three discrete progenitor domains across the dorsal‐ventral axis, with different domains responsible for producing unique autonomic or visceral motor neurons. Restricting the expression of mutant Phox2b to the ventral visceral motor neuron domain induces marked newborn apnea together with a significant loss of visceral motor neurons, RTN ablation, and preBötzinger complex dysfunction. This finding suggests that the observed apnea develops through non‐cell autonomous developmental mechanisms. Mutant Phox2b expression in dorsal rhombencephalic neurons did not generate significant respiratory dysfunction, but did result in subtle metabolic thermoregulatory deficiencies. We confirm the expression of a novel murine Phox2b splice variant which shares exons 1 and 2 with the more widely studied Phox2b splice variant, but which differs in exon 3 where most CCHS mutations occur. We also show that mutant Phox2b expression in the visceral motor neuron progenitor domain increases cell proliferation at the expense of visceral motor neuron development. We propose that visceral motor neurons may function as organizers of brainstem respiratory neuron development, and that disruptions in their development result in secondary/non‐cell autonomous maldevelopment of key brainstem respiratory neurons. [ABSTRACT FROM AUTHOR]

The healing time of burn wounds depends on surface area and depth of the burn and associated comorbidities. Diabetes mellitus (DM) causes delays in the healing process by extending the inflammatory phase. Treatment with topical insulin can improve the inflammatory phase, restore metabolic dysregulation, and modulate impaired cellular signaling in burn wounds. The objective of this study was to evaluate markers of the inflammatory and proliferative phases of second-degree burns after topical insulin treatment in diabetic rats. Type I DM was induced with streptozotocin in male Wistar rats. The animals' backs were shaved and subjected to thermal burning. Rats were randomized into two groups: control diabetic (DC) and insulin diabetic (DI). At Days 7 and 14 postburn, rats were euthanized, and wound-tissue sections were evaluated by hematoxylin and eosin, Weigert, and Verhöeff staining, immunohistochemistry-paraffin, and enzyme-linked immunosorbent assay. A significant increase in reepithelialization was seen on Days 7 and 14 in DI versus DC rats. On Day 7, interleukin (IL)-1β, IL-6, monocyte chemotactic protein (MCP)-1, and F4/80 expression were increased in DI versus DC rats. On Day 14, MCP-1 expression was decreased and F4/80 increased in DI versus DC rats. On Days 7 and 14, Ki-67, transforming growth factor-β1, vascular endothelial growth factor expression, and formation of elastic fibers were increased in DI versus DC rats. Topical insulin modulates burn-wound healing in diabetic animals by balancing inflammation and promoting angiogenesis and formation of elastic fibers. [ABSTRACT FROM AUTHOR]

Background: Cancer cachexia is a multifactorial syndrome that dramatically decreases survival. Loss of white adipose tissue (WAT) is one of the key characteristics of cachexia. WAT wasting is paralleled by microarchitectural remodeling in cachectic cancer patients. Fibrosis results from uncontrolled ECM synthesis, a process in which, transforming growth factor-beta (TGFβ) plays a pivotal role. So far, the mechanisms involved in adipose tissue (AT) re-arrangement, and the role of TGFβ in inducing AT remodeling in weight-losing cancer patients are poorly understood. This study examined the modulation of ECM components mediated by TGFβ pathway in fibrotic AT obtained from cachectic gastrointestinal cancer patients.Methods: After signing the informed consent form, patients were enrolled into the following groups: cancer cachexia (CC, n = 21), weight-stable cancer (WSC, n = 17), and control (n = 21). The total amount of collagen and elastic fibers in the subcutaneous AT was assessed by histological analysis and by immunohistochemistry. TGFβ isoforms expression was analyzed by Multiplex assay and by immunohistochemistry. Alpha-smooth muscle actin (αSMA), fibroblast-specific protein (FSP1), Smad3 and 4 were quantified by qPCR and/or by immunohistochemistry. Interleukin (IL) 2, IL5, IL8, IL13 and IL17 content, cytokines known to be associated with fibrosis, was measured by Multiplex assay.Results: There was an accumulation of collagen and elastic fibers in the AT of CC, as compared with WSC and controls. Collagens type I, III, VI, and fibronectin expression was enhanced in the tissue of CC, compared with both WSC and control. The pronounced expression of αSMA in the surrounding of adipocytes, and the increased mRNA content for FSP1 (20-fold) indicate the presence of activated myofibroblasts; particularly in CC. TGFβ1 and TGFβ3 levels were up-regulated by cachexia in AT, as well in the isolated adipocytes. Smad3 and Smad4 labeling was found to be more evident in the fibrotic areas of CC adipose tissue.Conclusions: Cancer cachexia promotes the development of AT fibrosis, in association with altered TGFβ signaling, compromising AT organization and function. [ABSTRACT FROM AUTHOR]

SARS-CoV2 infection causes COVID-19, and represents the most emergent health care crisis of our generation. Ample evidence in the scientific literature suggests that SARS-CoV, MERS-CoV, and endemic human coronaviruses infect brain cells. We delineate a rationale for encouraging evaluation of the brain, and in particular the brainstem, in COVID-19 so that potential neuropathological mechanisms can be delineated.

Cancer cachexia affects about 80% of advanced cancer patients, it is linked to poor prognosis and to date, there is no efficient treatment or cure. The syndrome leads to progressive involuntary loss of muscle and fat mass induced by systemic inflammatory processes. The role of the white adipose tissue (WAT) in the onset and manifestation of cancer cachexia gained importance during the last decade. WAT wasting is not only characterized by increased lipolysis and release of free fatty acids (FFA), but in addition, owing to its high capacity to produce a variety of inflammatory factors. The aim of this study was to characterize plasma lipid profile of cachectic patients and to correlate the FA composition with circulating inflammatory markers; finally, we sought to establish whether the fatty acids released by adipocytes trigger and/or contribute to local and systemic inflammation in cachexia. The study selected 65 patients further divided into 3 groups: control (N); weight stable cancer (WSC); and cachectic cancer (CC). The plasma FA profile was significantly different among the groups and was positively correlated with pro-inflammatory cytokines expression in the CC patients. Therefore, we propose that saturated to unsaturated FFA ratio may serve as a means of detecting cachexia., (Copyright © 2020 Riccardi, das Neves, de Matos-Neto, Camargo, Lima, Radloff, Alves, Costa, Tokeshi, Otoch, Maximiano, de Alcantara, Colquhoun, Laviano and Seelaender.)