Surface electromyographic evaluation of the neuromuscular activation of the inspiratory muscles during progressively increased inspiratory flow under inspiratory-resistive loading

This study aimed to evaluate neuromuscular activation in the scalene and sternocleidomastoid muscles using surface electromyography (EMG) during progressively increased inspiratory flow, produced by increasing the respiratory rate under inspiratory-resistive loading using a mask ventilator. Moreover, we attempted to identify the EMG inflection point (EMGIP) on the graph, at which the root mean square (RMS) of the EMG signal values of the inspiratory muscles against the inspiratory flow velocity acceleration abruptly increases, similarly to the EMG anaerobic threshold (EMGAT) reported during incremental-resistive loading in other skeletal muscles. We measured neuromuscular activation of healthy male subjects and found that the inspiratory flow velocity increased by approximately 1.6-fold. We successfully observed an increase in RMS that corresponded to inspiratory flow acceleration with ρ ≥ 0.7 (Spearman’s rank correlation) in 17 of 27 subjects who completed the experimental protocol. To identify EMGIP, we analyzed the fitting to either a straight or non-straight line related to the increasing inspiratory flow and RMS using piecewise linear spline functions. As a result, EMGIP was identified in the scalene and sternocleidomastoid muscles of 17 subjects. We believe that the identification of EMGIP in this study infers the existence of EMGAT in inspiratory muscles. Application of surface EMG, followed by identification of EMGIP, for evaluating the neuromuscular activation of respiratory muscles may be allowed to estimate the signs of the respiratory failure, including labored respiration, objectively and non-invasively accompanied using accessory muscles in clinical respiratory care

Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas

<p>Abstract</p> <p>Background</p> <p>The advent of gene expression profiling was expected to dramatically improve cancer diagnosis. However, despite intensive efforts and several successful examples, the development of profile-based diagnostic systems remains a difficult task. In the present work, we established a method to convert molecular classifiers based on adaptor-tagged competitive PCR (ATAC-PCR) (with a data format that is similar to that of microarrays) into classifiers based on real-time PCR.</p> <p>Methods</p> <p>Previously, we constructed a prognosis predictor for glioma using gene expression data obtained by ATAC-PCR, a high-throughput reverse-transcription PCR technique. The analysis of gene expression data obtained by ATAC-PCR is similar to the analysis of data from two-colour microarrays. The prognosis predictor was a linear classifier based on the first principal component (PC1) score, a weighted summation of the expression values of 58 genes. In the present study, we employed the delta-delta Ct method for measurement by real-time PCR. The predictor was converted to a Ct value-based predictor using linear regression.</p> <p>Results</p> <p>We selected <it>UBL5 </it>as the reference gene from the group of genes with expression patterns that were most similar to the median expression level from the previous profiling study. The number of diagnostic genes was reduced to 27 without affecting the performance of the prognosis predictor. PC1 scores calculated from the data obtained by real-time PCR showed a high linear correlation (r = 0.94) with those obtained by ATAC-PCR. The correlation for individual gene expression patterns (r = 0.43 to 0.91) was smaller than for PC1 scores, suggesting that errors of measurement were likely cancelled out during the weighted summation of the expression values. The classification of a test set (n = 36) by the new predictor was more accurate than histopathological diagnosis (log rank p-values, 0.023 and 0.137, respectively) for predicting prognosis.</p> <p>Conclusion</p> <p>We successfully converted a molecular classifier obtained by ATAC-PCR into a Ct value-based predictor. Our conversion procedure should also be applicable to linear classifiers obtained from microarray data. Because errors in measurement are likely to be cancelled out during the calculation, the conversion of individual gene expression is not an appropriate procedure. The predictor for gliomas is still in the preliminary stages of development and needs analytical clinical validation and clinical utility studies.</p

Autonomous growth potential of leukemia blast cells is associated with poor prognosis in human acute leukemias

We have described a severe combined immunodeficiency (SCID) mouse model that permits the subcutaneous growth of primary human acute leukemia blast cells into a measurable subcutaneous nodule which may be followed by the development of disseminated disease. Utilizing the SCID mouse model, we examined the growth potential of leukemic blasts from 133 patients with acute leukemia, (67 acute lymphoblastic leukemia (ALL) and 66 acute myeloid leukemia (AML)) in the animals after subcutaneous inoculation without conditioning treatment. The blasts displayed three distinct growth patterns: "aggressive", "indolent", or "no tumor growth". Out of 133 leukemias, 45 (33.8%) displayed an aggressive growth pattern, 14 (10.5%) displayed an indolent growth pattern and 74 (55.6%) did not grow in SCID mice. The growth probability of leukemias from relapsed and/or refractory disease was nearly 3 fold higher than that from patients with newly diagnosed disease. Serial observations found that leukemic blasts from the same individual, which did not initiate tumor growth at initial presentation and/or at early relapse, may engraft and grow in the later stages of disease, suggesting that the ability of leukemia cells for engraftment and proliferation was gradually acquired following the process of leukemia progression. Nine autonomous growing leukemia cell lines were established in vitro. These displayed an aggressive proliferation pattern, suggesting a possible correlation between the capacity of human leukemia cells for autonomous proliferation in vitro and an aggressive growth potential in SCID mice. In addition, we demonstrated that patients whose leukemic blasts displayed an aggressive growth and dissemination pattern in SClD mice had a poor clinical outcome in patients with ALL as well as AML. Patients whose leukemic blasts grew indolently or whose leukemia cells failed to induce growth had a significantly longer DFS and more favorable clinical course