Simulated microgravity triggers characteristic morphology and stress response in Saccharomyces cerevisiae.

Reduction of gravity results in changes in gene expression and morphology in the budding yeast Saccharomyces cerevisiae. We studied the genes responsible for the morphological changes induced by simulated microgravity (SMG) using the yeast morphology data. We comprehensively captured the features of the morphological changes in yeast cells cultured in SMG with CalMorph, a high-throughput image-processing system. Statistical analysis revealed that 95 of 501 morphological traits were significantly affected, which included changes in bud direction, the ratio of daughter to mother cell size, the random daughter cell shape, the large mother cell size, bright nuclei in the M phase, and the decrease in angle between two nuclei. We identified downregulated genes that impacted the morphological changes in conditions of SMG by focusing on each of the morphological features individually. Gene Ontology (GO)-enrichment analysis indicated that morphological changes under conditions of SMG were caused by cooperative downregulation of 103 genes annotated to six GO terms, which included cytoplasmic ribonucleoprotein granule, RNA elongation, mitotic cell cycle phase transition, nucleocytoplasmic transport, protein-DNA complex subunit organization, and RNA localization. P-body formation was also promoted under conditions of SMG. These results suggest that cooperative downregulation of multiple genes occurs in conditions of SMG.

Plasma myoglobin indicates muscle damage associated with acceleration/deceleration during football.

BACKGROUND: Monitoring muscle damage in athletes assists not only coaches to adjust the training workload but also medical staff to prevent injury. Measuring blood myoglobin concentration can help evaluate muscle damage. The novel portable device utilized in this study allows for easy on-site measurement of myoglobin, providing real-time data on the player's muscle damage. This study investigated the relationship between external load (global positioning system parameters) and internal loads (myoglobin concentration and creatine kinase activity) in 15 male professional football players before and after a match. METHODS: Whole blood samples from participants' fingertips were collected before the match (baseline) and at 2, 16, and 40 h after the match. Myoglobin concentrations were measured using the IA-100 compact immunoassay system. Creatine kinase concentrations were measured in a clinical laboratory, and match loads were monitored using a global positioning system device. RESULTS: The mean myoglobin concentration was significantly higher at 2 h than at the other time points (P<0.05), and decreased to baseline levels within 16 h post-match. The mean creatine kinase concentration increased after the match but did not reach a significant level. Muscle damage monitored by myoglobin after football match-play was strongly associated with acceleration/deceleration metrics rather than the sprint/high-speed running distance. CONCLUSIONS: Our findings indicate that myoglobin is a more sensitive marker of muscle damage than creatine kinase after football match-play. Monitoring myoglobin in athletes can aid in determining their recovery status from the previous training load and help practitioners manage the training load.

Improving the Accuracy of Diagnosis for Multiple-System Atrophy Using Deep Learning-Based Method.

Multiple-system atrophy (MSA) is primarily an autonomic disorder with parkinsonism or cerebellar ataxia. Clinical diagnosis of MSA at an early stage is challenging because the symptoms change over the course of the disease. Recently, various artificial intelligence-based programs have been developed to improve the diagnostic accuracy of neurodegenerative diseases, but most are limited to the evaluation of diagnostic imaging. In this study, we examined the validity of diagnosis of MSA using a pointwise linear model (deep learning-based method). The goal of the study was to identify features associated with disease differentiation that were found to be important in deep learning. A total of 3377 registered MSA cases from FY2004 to FY2008 were used to train the model. The diagnostic probabilities of SND (striatonigral degeneration), SDS (Shy-Drager syndrome), and OPCA (olivopontocerebellar atrophy) were estimated to be 0.852 ± 0.107, 0.650 ± 0.235, and 0.858 ± 0.270, respectively. In the pointwise linear model used to identify and visualize features involved in individual subtypes, autonomic dysfunction was found to be a more prominent component of SDS compared to SND and OPCA. Similarly, respiratory failure was identified as a characteristic of SDS, dysphagia was identified as a characteristic of SND, and brain-stem atrophy was identified as a characteristic of OPCA.

Correlation of the Blood Test Results Obtained between Assays Using Microliter-scale Fingertip Blood Samples Collected with a Novel Blood Collection Device and Conventional Venous Blood Assays: a Secondary Publication in English.

In this study, we investigated the correlations between biochemical and hematological test results obtained using microliter-scale fingertip blood samples collected with a newly developed blood collection device and those obtained using conventional venous blood. Eighty volunteer subjects were enrolled in this study. Blood samples were drawn from the fingertip of the ring finger by a single puncture, and 60-µL samples were promptly and accurately aspirated into a blood collection chip. Then the chip was tightly sealed in a chip container and was shaken to mix the contents without dispersion. For biochemical tests other than that for HbA1c, blood was collected without anticoagulant and centrifuged to obtain 15 µL of serum which was then diluted with 190 µL of physiological saline for the assay. For hematological tests and the test for HbA1c, the sample was assayed with blood collected using EDTA-2 K. Good correlations were obtained between the test results of the assay using fingertip blood and that using venous blood. The correlation coefficients were ≥0.97 for TG, T-CHO, HDL-C, LDL-C, GLU, ALT, γ-GTP, UA, BUN, and HbA1c and ≥0.95 for WBC, RBC, Hgb, and Hct. These results suggest that our microliter-scale blood testing system is comparable to assays using venous blood and may be useful as a rapid and simple test to determine basic clinical parameters that are close to the reference intervals.

Identification of pathogenesis-related microRNAs in hepatocellular carcinoma by expression profiling.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of the liver. Since postoperative recurrence and intrahepatic metastases occur frequently, the postoperative 5-year survival rate is low. To investigate the molecular mechanisms of HCC progression, mRNA as well as microRNA (miRNA) expression levels have been profiled in various studies. However, no previous study has comprehensively compared the expression of miRNAs in HCC patients with various clinical features using the tumor and surrounding non-tumor tissues and normal liver samples. In this study, we profiled the expression of miRNAs in tumor and non-tumor tissues from 40 HCC patients with heterogeneous pathogenesis and 6 surrounding non-tumor tissues from patients with metastatic liver cancer. To identify miRNAs specific to each disease state, we comprehensively compared the expression of miRNAs in various combinations. The results indicate that the expression of many known as well as novel miRNAs was altered in patients with the hepatitis C virus infection compared with those with the hepatitis B virus and without any virus infection. The following miRNAs were downregulated in the tumor and non-tumor tissues, and thus could serve as novel biomarkers for chronic liver diseases: miR-18b*, miR-296-5p, miR-557, miR-581, miR-625*, miR-1228, miR-1249 and miR-2116*. Similarly, miR-129*, miR-146b-3p and miR-448 are novel candidates for HCC biomarkers regardless of virus infection.

Genome-wide integrative analysis revealed a correlation between lengths of copy number segments and corresponding gene expression profile.

Microarray analysis has been applied to comprehensively reveal the abnormalities of DNA copy number (CN) and gene expression in human cancer research during the last decade. These analyses have individually contributed to identify the genes associated with carcinogenesis, progression, metastasis of tumor cells and poor prognosis of cancer patients. However, it is known that the correlation between profiles of CN and gene expression does not highly correlate. Factors which determine the degree of correlation remain largely unexplained. To investigate one such factor, we performed trend analyses between the lengths of CN segments and corresponding gene expression profiles from microarray data in hepatocellular carcinoma (HCC) and colorectal carcinoma (CRC). Significant correlations were observed in CN gain of HCC and CRC (p<0.05). The trend of the CN loss showed a significant correlation in HCC although there was no correlation between the length of CN loss segments and gene expression in CRC. Our findings suggest that the influence of CN on gene expression highly depends on the length of CN region, especially in the case of CN gain. To the best of our knowledge, this is the first study describing the correlation between lengths of CNA segments and expression profiles of corresponding genes.