The vestibular system is critical for the changes in muscle and bone induced by hypergravity in mice

Gravity changes concurrently affect muscle and bone as well as induce alterations in vestibular signals. However, the role of vestibular signals in the changes in muscle and bone induced by gravity changes remains unknown. We therefore investigated the effects of vestibular lesions (VL) on the changes in muscle and bone induced by 3 g hypergravity for 4 weeks in C57BL/6J mice. Quantitative computed tomography analysis revealed that hypergravity increased muscle mass surrounding the tibia and trabecular bone mineral content, adjusting for body weight in mice. Hypergravity did not affect cortical bone and fat masses surrounding the tibia. Vestibular lesions blunted the increases in muscle and bone masses induced by hypergravity. Histological analysis showed that hypergravity elevated the cross‐sectional area of myofiber in the soleus muscle. The mRNA levels of myogenic genes such as MyoD, Myf6, and myogenin in the soleus muscle were elevated in mice exposed to hypergravity. Vestibular lesions attenuated myofiber size and the mRNA levels of myogenic differentiation markers enhanced by hypergravity in the soleus muscle. Propranolol, a β‐blocker, antagonized the changes in muscle induced by hypergravity. In conclusion, this study is the first to demonstrate that gravity changes affect muscle and bone through vestibular signals and subsequent sympathetic outflow in mice

Changes in Salmon GnRH and Chicken GnRH-II Contents in the Brain and Pituitary, and GTH Contents in the Pituitary in Female Masu Salmon, Oncorhynchus masou, from Hatching through Ovulation(Endocrinology)

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Roles of the vestibular system in obesity and impaired glucose metabolism in high-fat diet-fed mice.

The vestibular system controls balance, posture, blood pressure, and gaze. However, the roles of the vestibular system in energy and glucose metabolism remain unknown. We herein examined the roles of the vestibular system in obesity and impaired glucose metabolism using mice with vestibular lesions (VL) fed a high-sucrose/high-fat diet (HSHFD). VL was induced by surgery or arsenic. VL significantly suppressed body fat enhanced by HSHFD in mice. Glucose intolerance was improved by VL in mice fed HSHFD. VL blunted the levels of adipogenic factors and pro-inflammatory adipokines elevated by HSHFD in the epididymal white adipose tissue of mice. A β-blocker antagonized body fat and glucose intolerance enhanced by HSHFD in mice. The results of an RNA sequencing analysis showed that HSHFD induced alterations in genes, such as insulin-like growth factor-2 and glial fibrillary acidic protein, in the vestibular nuclei of mice through the vestibular system. In conclusion, we herein demonstrated that the dysregulation of the vestibular system influences an obese state and impaired glucose metabolism induced by HSHFD in mice. The vestibular system may contribute to the regulation of set points under excess energy conditions

Role of Macrophages and Plasminogen Activator Inhibitor-1 in Delayed Bone Repair Induced by Glucocorticoids in Mice

Glucocorticoids delay fracture healing and induce osteoporosis. However, the mechanisms by which glucocorticoids delay bone repair have yet to be clarified. Plasminogen activator inhibitor-1 (PAI-1) is the principal inhibitor of plasminogen activators and an adipocytokine that regulates metabolism. We herein investigated the roles of macrophages in glucocorticoid-induced delays in bone repair after femoral bone injury using PAI-1-deficient female mice intraperitoneally administered with dexamethasone (Dex). Dex significantly decreased the number of F4/80-positive macrophages at the damaged site two days after femoral bone injury. It also attenuated bone injury-induced decreases in the number of hematopoietic stem cells in bone marrow in wild-type and PAI-1-deficient mice. PAI-1 deficiency significantly weakened Dex-induced decreases in macrophage number and macrophage colony-stimulating factor (M-CSF) mRNA levels at the damaged site two days after bone injury. It also significantly ameliorated the Dex-induced inhibition of macrophage phagocytosis at the damaged site. In conclusion, we herein demonstrated that Dex decreased the number of macrophages at the damaged site during early bone repair after femoral bone injury partly through PAI-1 and M-CSF in mice

A machine learning-based treatment prediction model using whole genome variants of hepatitis C virus.

In recent years, the development of diagnostics using artificial intelligence (AI) has been remarkable. AI algorithms can go beyond human reasoning and build diagnostic models from a number of complex combinations. Using next-generation sequencing technology, we identified hepatitis C virus (HCV) variants resistant to directing-acting antivirals (DAA) by whole genome sequencing of full-length HCV genomes, and applied these variants to various machine-learning algorithms to evaluate a preliminary predictive model. HCV genomic RNA was extracted from serum from 173 patients (109 with subsequent sustained virological response [SVR] and 64 without) before DAA treatment. HCV genomes from the 109 SVR and 64 non-SVR patients were randomly divided into a training data set (57 SVR and 29 non-SVR) and a validation-data set (52 SVR and 35 non-SVR). The training data set was subject to nine machine-learning algorithms selected to identify the optimized combination of functional variants in relation to SVR status following DAA therapy. Subsequently, the prediction model was tested by the validation-data set. The most accurate learning method was the support vector machine (SVM) algorithm (validation accuracy, 0.95; kappa statistic, 0.90; F-value, 0.94). The second-most accurate learning algorithm was Multi-layer perceptron. Unfortunately, Decision Tree, and Naive Bayes algorithms could not be fitted with our data set due to low accuracy (< 0.8). Conclusively, with an accuracy rate of 95.4% in the generalization performance evaluation, SVM was identified as the best algorithm. Analytical methods based on genomic analysis and the construction of a predictive model by machine-learning may be applicable to the selection of the optimal treatment for other viral infections and cancer