A possible new approach in the prediction of late gestational hypertension: The role of the fetal aortic intima-media thickness

The aim was to determine the predictive role of combined screening for late-onset gestational hypertension by fetal ultrasound measurements, third trimester uterine arteries (UtAs) Doppler imaging, and maternal history. This prospective study on singleton pregnancies was conducted at the tertiary center of Maternal and Fetal Medicine of the University of Padua during the period between January 2012 and December 2014. Ultrasound examination (fetal biometry, fetal wellbeing, maternal Doppler study, fetal abdominal aorta intima-media thickness [aIMT], and fetal kidney volumes), clinical data (mother age, prepregnancy body mass index [BMI], and parity), and pregnancy outcomes were collected. The P value <0.05 was defined significant considering a 2-sided alternative hypothesis. The distribution normality of variables were assessed using Kolmogorov-Smirnoff test. Data were presented by mean (±standard deviation), median and interquartile range, or percentage and absolute values. We considered data from 1381 ultrasound examinations at 29 to 32 weeks’ gestation, and in 73 cases late gestational hypertension developed after 34 weeks’ gestation. The final multivariate model found that fetal aIMT as well as fetal umbilical artery pulsatility index (PI), maternal age, maternal prepregnacy BMI, parity, and mean PI of maternal UtAs, assessed at ultrasound examination of 29 to 32 weeks’ gestation, were significant and independent predictors for the development of gestational hypertension after 34 weeks’ gestation. The area under the curve of the model was 81.07% (95% confidence interval, 75.83%-86.32%). A nomogram was developed starting from multivariate logistic regression coefficients. Late-gestational hypertension could be independently predicted by fetal aIMT assessment at 29 to 32 weeks’ gestation, ultrasound Doppler waveforms, and maternal clinical parameters. Copyright © 2017 the Author(s). Published by Wolters Kluwer Health, Inc

Growth abnormalities of fetuses and infants

The objective of this special issue is to address recent research trends and developments about the advancements of image processing and vision in healthcare. A substantial number of papers were submitted, and after a thorough peer review process, some of these were selected to be included in this special issue. Growth abnormalities (either growth restriction or large for gestational age) during perinatal and postnatal life are a hot topic issue, since they are often linked to alteration of uterine environment caused by placental insufficiency, maternal metabolic syndrome, and in general under- or overnutrition of the fetus. These fetal abnormalities account for the leading causes of perinatal morbidity and mortality. Moreover, under the hypothesis of developmental origin of adult diseases, they bear consequences in later life, programming the infant physiology for a higher risk of noncommunicable diseases, cardiovascular adult diseases, and neurodevelopment delay. Low birth weight, caused either by preterm birth and/or by intrauterine growth restriction, is recently known to be associated with increased rates of cardiovascular disease and noninsulin dependent diabetes in adult life. The “developmental origins of adult disease” hypothesis, often called “the Barker hypothesis,” proposes that these diseases originate through adaptations of the fetus when it is undernourished. These adaptations may be cardiovascular, metabolic, or endocrine and they may permanently change the structure and function of the body, increasing coronary heart disease risk factors, such as hypertension, type 2 diabetes mellitus, insulin resistance, and hyperlipidaemia. This hypothesis originally involved from observation by Barker and colleagues that the regions in England with the highest rates of infant mortality in the early 20th century also had the highest rates of mortality from coronary heart disease decades later. As the most commonly registered cause of infant death at the start of 20th century was low birth weight, these observations led to the hypothesis that low birth weight babies who survived infancy and childhood might be at increased risk of coronary heart disease in later life. There is an increased evidence of the link between intrauterine and perinatal alterations and adult diseases. Although the main focus so far has been the timing of delivery and follow-up, the study of the pathophysiology and of possible recovery is of paramount importance and needs the contributions of physicians from several fields, biologists, bioinformaticians, and engineers

Octopus-Inspired Suction Cups with Embedded Strain Sensors for Object Recognition

The octopus has unique capacities are sources of inspiration in developing soft robotic-enabling technologies. Herein, soft, sensorized, suction cups inspired by the suckers of Octopus vulgaris are presented. The suction cups using direct casting are fabricated, so that materials with different mechanical properties can be combined to optimize sensing and grasping capabilities. The artificial suckers integrate four embedded strain sensors, individually characterized and placed in a 90 degrees configuration along the rim of the suction cup. Based on this arrangement, how well the sensory suction cup can detect 1) the direction and 2) the angle (from 30 degrees to 90 degrees) of a touched inclined surface and 3) the stiffness of a touched flat object (shore hardness between 0010 and D50) both in air and underwater is evaluated. Data processing on neural networks is based using a multilayer perceptron to perform regression on individual properties. The results show a mean absolute error of 0.98 for angles, 0.02 for directions, and 97.9% and 93.5% of accuracy for the material classification in air and underwater, respectively. In view of the results and scalability in manufacturing, the proposed artificial suckers would seem to be highly effective solutions for soft robotics, including blind exploration and object recognition

Real-time diameter of the fetal aorta from ultrasound

The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. This article presents an attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. The solution is investigated with a cohort of 25 ultrasound sequences acquired during the third-trimester pregnancy check, and with 1000 synthetic sequences. In the extraction of features, it is shown that a shallow CNN outperforms two other deep CNNs with both the real and synthetic cohorts, suggesting that echocardiographic features are optimally captured by a reduced number of CNN layers. The proposed architecture, working with the shallow CNN, reaches an accuracy substantially superior to previously reported methods, providing an average reduction of the mean squared error from 0.31 (state-of-the-art) to 0.09 $$ackslashmathrmmm^2$$mm2, and a relative error reduction from 8.1 to 5.3%. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real-time clinical use

Noise, age and gender effects on speech intelligibility and sentence comprehension for 11- to 13-year-old children in real classrooms.

The present study aimed to investigate the effects of type of noise, age, and gender on children\u2019s speech intelligibility (SI) and sentence comprehension (SC). The experiment was conducted with 171 children between 11 and 13 years old in ecologically-valid conditions (collective presentation in real, reverberating classrooms). Two standardized tests were used to assess SI and SC. The two tasks were presented in three listening conditions: quiet; traffic noise; and classroom noise (non-intelligible noise with the same spectrum and temporal envelope of speech, plus typical classroom sound events). Both task performance accuracy and listening effort were considered in the analyses, the latter tracked by recording the response time (RT) using a single-task paradigm. Classroom noise was found to have the worst effect on both tasks (worsening task performance accuracy and slowing RTs), due to its spectro-temporal characteristics. A developmental effect was seen in the range of ages (11\u201313 years), which depended on the task and listening condition. Gender effects were also seen in both tasks, girls being more accurate and quicker to respond in most listening conditions. A significant interaction emerged between type of noise, age and task, indicating that classroom noise had a greater impact on RTs for SI than for SC. Overall, these results indicate that, for 11- to 13-year-old children, performance in SI and SC tasks is influenced by aspects relating to both the sound environment and the listener (age, gender). The presence of significant interactions between these factors and the type of task suggests that the acoustic conditions that guarantee optimal SI might not be equally adequate for SC. Our findings have implications for the development of standard requirements for the acoustic design of classrooms

Ultrahigh finesse Fabry-Perot superconducting resonator

We have built a microwave Fabry-Perot resonator made of diamond-machined copper mirrors coated with superconducting niobium. Its damping time (Tc = 130 ms at 51 GHz and 0.8 K) corresponds to a &#64257;nesse of 4.6 x 109, the highest ever reached for a Fabry-Perot in any frequency range. This result opens novel perspectives for quantum information, decoherence and non-locality studies

Being honest with causal language in writing for publication

The misleading use of causal language in publication is problematic for authors, reviewers and consumers of the information. Published research in quality journals has important knowledge implications and it is, therefore, contingent on authors to use language that is accurate and appropriate to their work. Language implying unsupported causal relationships may overstate the evidence-base, especially if accepted by uncritical readers or unwitting members of the general public who may not understand how to interpret inferential statistics