Transcriptomic analysis of the cerebral hippocampal tissue in spontaneously hypertensive rats exposed to acute hypobaric hypoxia: associations with inflammation and energy metabolism.

We evaluated the effect of acute hypobaric hypoxia (AHH) on the hippocampal region of the brain in early-stage spontaneously hypertensive male rats. The rats were classified into a control (ground level; ~ 400 m altitude) group and an AHH experimental group placed in an animal hypobaric chamber at a simulated altitude of 5500 m for 24 h. RNA-Seq analysis of the brains and hippocampi showed that differentially expressed genes (DEGs) were primarily associated with ossification, fibrillar collagen trimer, and platelet-derived growth factor binding. The DEGs were classified into functional categories including general function prediction, translation, ribosomal structure and biogenesis, replication, recombination, and repair. Pathway enrichment analysis revealed that the DEGs were primarily associated with relaxin signaling, PI3K-Akt signaling, and amoebiasis pathways. Protein-protein interaction network analysis indicated that 48 DEGs were involved in both inflammation and energy metabolism. Further, we performed validation experiments to show that nine DEGs were closely associated with inflammation and energy metabolism, of which two (Vegfa and Angpt2) and seven (Acta2, Nfkbia, Col1a1, Edn1, Itga1, Ngfr, and Sgk1) genes showed up and downregulated expression, respectively. Collectively, these results indicated that inflammation and energy metabolism-associated gene expression in the hippocampus was altered in early-stage hypertension upon AHH exposure.

Simulated vestibular spatial disorientation mouse model under coupled rotation revealing potential involvement of Slc17a6.

Spatial disorientation (SD) is the main contributor to flight safety risks, but research progress in animals has been limited, impeding a deeper understanding of the underlying mechanisms of SD. This study proposed a method for constructing and evaluating a vestibular SD mouse model, which adopted coupled rotational stimulation with visual occlusion. Physiological parameters were measured alongside behavioral indices to assess the model, and neuronal changes were observed through immunofluorescent staining. The evaluation of the model involved observing decreased colonic temperature and increased arterial blood pressure in mice exposed to SD, along with notable impairments in motor and cognitive function. Our investigation unveiled that vestibular SD stimulation elicited neuronal activation in spatially associated cerebral areas, such as the hippocampus. Furthermore, transcriptomic sequencing and bioinformatics analysis revealed the potential involvement of Slc17a6 in the mechanism of SD. These findings lay a foundation for further investigation into the molecular mechanisms underlying SD.

Multi-Dimensional and Objective Assessment of Motion Sickness Susceptibility Based on Machine Learning.

Background: As human transportation, recreation, and production methods change, the impact of motion sickness (MS) on humans is becoming more prominent. The susceptibility of people to MS can be accurately assessed, which will allow ordinary people to choose comfortable transportation and entertainment and prevent people susceptible to MS from entering provocative environments. This is valuable for maintaining public health and the safety of tasks. Objective: To develop an objective multi-dimensional MS susceptibility assessment model based on physiological indicators that objectively reflect the severity of MS and provide a reference for improving the existing MS susceptibility assessment methods. Methods: MS was induced in 51 participants using the Coriolis acceleration stimulation. Some portable equipment were used to digitize the typical clinical manifestations of MS and explore the correlations between them and Graybiel's diagnostic criteria. Based on significant objective parameters and selected machine learning (ML) algorithms, several MS susceptibility assessment models were developed, and their performances were compared. Results: Gastric electrical activity, facial skin color, skin temperature, and nystagmus are related to the severity of MS. Among the ML assessment models based on these variables, the support vector machine classifier had the best performance with an accuracy of 88.24%, sensitivity of 91.43%, and specificity of 81.25%. Conclusion: The severity of symptoms and signs of MS can be objectively quantified using some indicators. Multi-dimensional and objective assessment models for MS susceptibility based on ML can be successfully established.

High-Throughput Sequencing Reveals CXCR4 and IGF1 Behave Different Roles in Weightlessness Osteoporosis.

Objective: This study is aimed at screening the differential expression profiles of mRNA under weightlessness osteoporosis through high-throughput sequencing technology, as well as investigating the pathogenesis of weightlessness osteoporosis at the molecular level especially in bone marrow mesenchymal stem cells (BMSCs). Methods: The mouse bone marrow mesenchymal stem cell line was divided into ground group and simulated microgravity (SMG) group. BMP-2 was used to induce osteogenic differentiation, and SMG group was placed into 2D-gyroscope to simulate weightless condition. Transcriptome sequencing was performed by Illumina technology, DEGs between ground and SMG group was conducted using the DEseq2 algorithm. Molecular functions and signaling pathways enriched by DEGs were then comprehensively analyzed via multiple bioinformatic approaches including but not limited to GO, KEGG, GSEA, and PPI analysis. Results: A total of 263 DEGs were identified by comparing these 2 groups, including 186 upregulated genes and 77 downregulated genes. GO analysis showed that DEGs were enriched in osteoblasts, osteoclasts cell proliferation, differentiation, and apoptosis; KEGG analysis revealed that DEGs were significantly enriched in the TNF signaling pathway and FoxO signaling pathway; the enrichment results from Reactome database displayed that DEGs were mainly involved in the transcription of Hoxb3 gene, RUNX1 recruitment KMT2A gene, and activation of Hoxa2 chromatin signaling pathway. The four genes, IL6, CXCR4, IGF1, and PLOD2, were identified as hub genes for subsequent analysis. Conclusions: This study elucidated the significance of 10 hub genes in the development of weightlessness osteoporosis. In addition, the results of this study provide a theoretical basis and novel ideas for the subsequent research of the pathogenesis and clinical treatment of weightlessness osteoporosis.