Nodal-Dependent Mesendoderm Specification Requires the Combinatorial Activities of FoxH1 and Eomesodermin

Vertebrate mesendoderm specification requires the Nodal signaling pathway and its transcriptional effector FoxH1. However, loss of FoxH1 in several species does not reliably cause the full range of loss-of-Nodal phenotypes, indicating that Nodal signals through additional transcription factors during early development. We investigated the FoxH1-dependent and -independent roles of Nodal signaling during mesendoderm patterning using a novel recessive zebrafish FoxH1 mutation called midway, which produces a C-terminally truncated FoxH1 protein lacking the Smad-interaction domain but retaining DNA–binding capability. Using a combination of gel shift assays, Nodal overexpression experiments, and genetic epistasis analyses, we demonstrate that midway more accurately represents a complete loss of FoxH1-dependent Nodal signaling than the existing zebrafish FoxH1 mutant schmalspur. Maternal-zygotic midway mutants lack notochords, in agreement with FoxH1 loss in other organisms, but retain near wild-type expression of markers of endoderm and various nonaxial mesoderm fates, including paraxial and intermediate mesoderm and blood precursors. We found that the activity of the T-box transcription factor Eomesodermin accounts for specification of these tissues in midway embryos. Inhibition of Eomesodermin in midway mutants severely reduces the specification of these tissues and effectively phenocopies the defects seen upon complete loss of Nodal signaling. Our results indicate that the specific combinations of transcription factors available for signal transduction play critical and separable roles in determining Nodal pathway output during mesendoderm patterning. Our findings also offer novel insights into the co-evolution of the Nodal signaling pathway, the notochord specification program, and the chordate branch of the deuterostome family of animals

A Notch updated

Cell–cell signaling mediated by the Notch receptor is iteratively involved in numerous developmental contexts, and its dysregulation has been associated with inherited genetic disorders and cancers. The core components of the signaling pathway have been identified for some time, but the study of the modulation of the pathway in different cellular contexts has revealed many layers of regulation. These include complex sugar modifications in the extracellular domain as well as transit of Notch through defined cellular compartments, including specific endosomes

Macrosomia and large for gestational age in Asia:One size does not fit all

Macrosomia, usually defined as infant birth weight of >= 4000 g, does not consider gestational age, sex, or country/region-specific differences in mean birth weight and maternal body weight. This issue is particularly relevant for Asia, where 60% of the world's population lives, due to variations in maternal size and birth weights across populations. Large for gestational age (LGA), defined as birth weight > 90th centile, is a more sensitive measure as it considers gestational age and sex, though it is dependent on the choice of growth charts. We aimed to review reporting of macrosomia and LGA in Asia. We reviewed the literature on prevalence and risk of macrosomia and LGA in Asia over the last 29 years. Prevalence of macrosomia ranged from 0.5% (India) to 13.9% (China) while prevalence of LGA ranged from 4.3% (Korea) to 22.1% (China), indicating substantial variation in prevalence within and between Asian countries. High pre-pregnancy body mass index, excessive gestational weight gain, and impaired glucose tolerance conferred risk of macrosomia/LGA. Incidence of macrosomia and LGA varies substantially within and between Asian countries, as do the growth charts and definitions. The latter makes it impossible to make comparisons but suggests differences in intrauterine growth between populations. Reporting LGA, using standardized country/regional growth charts, would better capture the incidence of high birth weight and allow for comparison and identification of contributing factors. Better understanding of local drivers of excessive intrauterine growth could enable development of improved strategies for prevention and management of LGA

Assurance of Medical Device Quality with Quality Management System: An Analysis of Good Manufacturing Practice Implementation in Taiwan

The implementation of an effective quality management system has always been considered a principal method for a manufacturer to maintain and improve its product and service quality. Globally many regulatory authorities incorporate quality management system as one of the mandatory requirements for the regulatory control of high-risk medical devices. The present study aims to analyze the GMP enforcement experience in Taiwan between 1998 and 2013. It describes the regulatory implementation of medical device GMP requirement and initiatives taken to assist small and medium-sized enterprises in compliance with the regulatory requirement. Based on statistical data collected by the competent authority and industry research institutes, the present paper reports the growth of Taiwan local medical device industry after the enforcement of GMP regulation. Transition in the production, technologies, and number of employees of Taiwan medical device industry between 1998 and 2013 provides the competent authorities around the world with an empirical foundation for further policy development

The effect of lifestyle on late-life cognitive change under different socioeconomic status.

This study aimed to identify lifestyle factors associated with cognitive change and to explore whether the effect of lifestyle varies by socioeconomic status (SES). Participants aged 65 years and older were recruited from elderly health checkup programs from 2011 to 2013 in Taiwan. Neuropsychological tests, including tests of global cognition, logical memory, executive function, verbal fluency and attention, were administered at baseline (N = 603) and 2 years later (N = 509). After literature review, 9 lifestyle factors and 3 SES indicators were chosen and their effects on cognitive change were evaluated using linear regression adjusting for age, sex, education, APOE ε4 status, and baseline cognitive score. Five lifestyle factors (high vegetable and fish intake, regular exercise, not smoking, and light to moderate alcohol consumption) and 3 SES indicators [annual household income (> 33,333 USD vs. less), occupational complexity (high vs. low mental demanding job), and years of education (> 12 years vs. less)] were found to be protective against cognitive decline (P < 0.1 in any cognitive domains, ß ranging from 0.06 to 0.38). After further adjusting for all the lifestyle and SES factors, fish intake, higher income and occupational complexity remained protective. Significant interactions were found between a healthful lifestyle (defined as having ≥ 3 healthful lifestyle factors) and income on changes of global cognition and verbal fluency (Pinteraction = 0.02 and 0.04). The protective effect of a healthful lifestyle was observed only among participants with lower income in global cognition and logical memory [ß = 0.17, 95% confidence interval (CI) = 0.07-0.26; ß = 0.30, 95% CI = 0.14-0.46]. To the best of our knowledge, this study for the first time explored how the interactions of lifestyle and SES affect cognitive change. Our findings will aid in developing dementia prevention programs and reduce health inequalities

Interactions between a healthful lifestyle and annual household income on cognitive change.

<p><i>P</i>_{<i>interaction</i>} is presented in the middle of the figure. After stratification by annual household income, the effects of having a healthful lifestyle compared with an unhealthful lifestyle are shown as β-coefficients (95% CI) above the solid lines after adjustment for age, sex, number of years of education, <i>APOE</i> ε4 status, baseline cognitive domain score, CES-D score, hypertension, diabetes mellitus, stroke, daily energy intake, and occupational complexity. The solid lines represent the cognitive change values for participants with a healthful lifestyle (having ≥ 3 healthful lifestyle factors), whereas the dashed lines represent individuals with an unhealthful lifestyle (having 0–2 healthful lifestyle factors). CI = confidence interval; Ref. = reference group. ***<i>P</i> < 0.001.</p

Effects of different lifestyle factors and SES indicators on cognitive change.

<p>Effects of different lifestyle factors and SES indicators on cognitive change.</p