Pulmonary Vascular Regulation in the Fetal and Transitional Lung.

Fetal lungs have fewer and smaller arteries with higher pulmonary vascular resistance (PVR) than a newborn. As gestation advances, the pulmonary circulation becomes more sensitive to changes in pulmonary arterial oxygen tension, which prepares them for the dramatic drop in PVR and increase in pulmonary blood flow (PBF) that occur when the baby takes its first few breaths of air, thus driving the transition from fetal to postnatal circulation. Dynamic and intricate regulatory mechanisms control PBF throughout development and are essential in supporting gas exchange after birth. Understanding these concepts is crucial given the role the pulmonary vasculature plays in the development of complications with transition, such as in the setting of persistent pulmonary hypertension of the newborn and congenital heart disease. An improved understanding of pulmonary vascular regulation may reveal opportunities for better clinical management.

New Features for the Detection of Fetal QRS Complexes in Non-Invasive Fetal Electrocardiograms.

Non-invasive fetal electrocardiography (NI-fECG) is a promising technique for continuous fetal heart rate (fHR) monitoring. However, the weak amplitude of the fetal electrocardiogram (fECG), and the presence of the dominant maternal ECG (mECG), makes it highly challenging to detect the fetal QRS (fQRS) complex, which is needed to obtain the fHR. This paper proposes a new method for automated fQRS detection from single-channel NI-fECG signals, without cancelling out the mECG. The proposed method leverages the different spectral behaviour exhibited by mECG and fECG signals. Fetal R-peaks are detected using a hybrid combination of k-means clustering with time and time-frequency features extracted from pre-processed NI-fECG recordings. The performance of our method is evaluated using real and synthetic signals from publicly available datasets, achieving a best of 96.3% sensitivity and 90.4% F1 score. The results obtained demonstrates the effectiveness of the proposed method for the detection of fQRS complexes with high sensitivity and low computational complexity.

Prediction of fetal RR intervals from maternal factors using machine learning models.

Previous literature has highlighted the importance of maternal behavior during the prenatal period for the upbringing of healthy adults. During pregnancy, fetal health assessments are mainly carried out non-invasively by monitoring fetal growth and heart rate (HR) or RR interval (RRI). Despite this, research entailing prediction of fHRs from mHRs is scarce mainly due to the difficulty in non-invasive measurements of fetal electrocardiogram (fECG). Also, so far, it is unknown how mHRs are associated with fHR over the short term. In this study, we used two machine learning models, support vector regression (SVR) and random forest (RF), for predicting average fetal RRI (fRRI). The predicted fRRI values were compared with actual fRRI values calculated from non-invasive fECG. fRRI was predicted from 13 maternal features that consisted of age, weight, and non-invasive ECG-derived parameters that included HR variability (HRV) and R wave amplitude variability. 156 records were used for training the models and the results showed that the SVR model outperformed the RF model with a root mean square error (RMSE) of 29 ms and an average error percentage (< 5%). Correlation analysis between predicted and actual fRRI values showed that the Spearman coefficient for the SVR and RF models were 0.31 (P < 0.001) and 0.19 (P < 0.05), respectively. The SVR model was further used to predict fRRI of 14 subjects who were not included in the training. The latter prediction results showed that individual error percentages were (≤ 5%) except in 3 subjects. The results of this study show that maternal factors can be potentially used for the assessment of fetal well-being based on fetal HR or RRI.

Contemporary Understanding of the Central Autonomic Nervous System in Fetal-Neonatal Transition.

THE CRITICAL ROLE OF THE CENTRAL AUTONOMIC NERVOUS SYSTEM IN FETAL-NEONATAL TRANSITION: Sarah B. Mulkey, Adre dú Plessis Seminars in Pediatric Neurology Volume 28, December 2018, Pages 29-37 The objective of this article is to understand the complex role of the central autonomic nervous system in normal and complicated fetal-neonatal transition and how autonomic nervous system dysfunction can lead to brain injury. The central autonomic nervous system supports coordinated fetal transitional cardiovascular, respiratory, and endocrine responses to provide safe transition of the fetus at delivery. Fetal and maternal medical and environmental exposures can disrupt normal maturation of the autonomic nervous system in utero, cause dysfunction, and complicate fetal-neonatal transition. Brain injury may both be caused by autonomic nervous system failure and contribute directly to autonomic nervous system dysfunction in the fetus and newborn. The central autonomic nervous system has multiple roles in supporting transition of the fetus. Future studies should aim to improve real-time monitoring of fetal autonomic nervous system function and in supporting typical autonomic nervous system development even under complicated conditions.

Maternal mood and anxiety effects on the fetal nonstress test.

BACKGROUND: Previous studies have suggested that acute mood states may influence levels of fetal activity. Because the fetal nonstress test relies on markers of fetal activity to suggest fetal wellbeing, its interpretation may be affected by maternal mood. OBJECTIVE: This study sought to determine if there are differences in nonstress test characteristics between pregnant individuals with and without symptoms of mood disorder. STUDY DESIGN: In this prospective cohort study, we recruited pregnant individuals undergoing nonstress test in the third trimester and compared the results of the nonstress test between pregnant individuals with scores above and below the cutoff values on validated screening questionnaires for depression and anxiety symptoms, the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7). Demographic information was collected from each participant at the time of recruitment, and medical information was extracted from the electronic medical record. RESULTS: A total of 68 pregnant individuals were enrolled, 10 (15%) of which screened positive for perinatal mood disorders. There was no significant difference in means of time to reactivity (15.6 [4.8] minutes vs 15.0 [8.0] minutes, P=.77), number of accelerations (0.16/min [0.08] vs 0.16/min [0.10], P>.95), number of fetal movements (17.0 [14.7] vs 19.7 [20.4], P=.62), heart rate baseline (138.0 [7.5] bpm vs 139.2 [9.0] bpm, P=.67) or variability (8.5 [2.5] bpm vs 9.1 (4.3) bpm, P=.51) between pregnant individuals who screen positive for mood disorders and those who did not. CONCLUSION: Fetal heart rate patterns are similar in pregnant individuals with and without symptoms of mood disorder. The results provide reassurance that acute symptoms of anxiety and depression do not have significant effects on the fetal nonstress test.

In Vivo Real-Time Study of Drug Effects on Carotid Blood Flow in the Ovine Fetus.

The ability of an organism to maintain a constant blood flow to the brain in response to sudden surges in systemic blood pressure (BP) is known as cerebral autoregulation (CAR), which occurs in the carotid artery. In contrast to full-term neonates, preterm neonates are unable to reduce the cerebral blood flow (CBF) in response to increased systemic BP. In preterm neonates, this exposes the fragile cerebral vessels to high perfusion pressures, leading to their rupture and brain damage. Ex vivo studies using wire myography have demonstrated that carotid arteries from near-term fetuses constrict in response to the activation of adrenergic alpha1 receptors. This response is blunted in the preterm fetus. Thus, to examine the role of alpha1-AR in vivo, presented here is an innovative approach to determine the effects of drugs on a carotid arterial segment in vivo in an ovine fetus during the developmental progression of gestation. The presented data demonstrate the simultaneous measurement of fetal blood flow and blood pressure. The perivascular delivery system can be used to conduct a long-term study over several days. Additional applications for this method could include viral delivery systems to alter the expression of genes in a segment of the carotid artery. These methods could be applied to other blood vessels in the growing organism in utero as well as in adult organisms.

Identifying fetal status with fetal heart rate: Deep learning approach based on long convolution.

CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fetal heart rate). The rapid development of Artificial Intelligence has led realization of computer-aided CTG technology, Intelligent CTG classification based on FHR is a fundamental component of these technologies. Its implementation can provide doctors with auxiliary decisions. Most of existing FHR classification methods are based on combing different deep learning models, such as CNN (Convolutional Neural Network), LSTM (Long short-term memory) and Transformer. However, these studies ignore the balance of positive and negative samples in dataset and the matching degree between model and FHR classification task, which reduces the classification accuracy. In this paper, we mainly discuss two major problems in previous FHR classification studies: reduce class imbalance and select appropriate convolution kernel. To address above two problems, we propose a data augmentation method based on ECMN (Edge Clipping and Multiscale Noise) to resolve class imbalance. Subsequently, we introduce a one-dimensional long convolutional layer, which use trend area to calculate the appropriate convolution kernel. Based on appropriate convolution kernel, an improved residual structure with attention mechanism named TGLCN (Trend-Guided Long Convolution Network) is proposed to improve FHR classification accuracy. Finally, horizontal and longitudinal experiments show that the TGLCN obtains high classification accuracy and speed of parameter adjustment.

Influence of fetal hiccups on Doppler blood flow waveform of fetal arteries: Difference among arteries.

BACKGROUND: A few studies have been reported on the influence of fetal hiccups on umbilical artery. The aim of this study is to clarify the influence of fetal hiccups on Doppler blood flow waveform (DBFW) of some fetal arteries, and to show the difference in these influences among fetal arteries. OBJECTIVE: DBFW of umbilical artery, descending aorta, and middle cerebral artery were recorded at hiccups in normal fetuses between 34th and 40th gestational weeks. The changes on DBFW were classified into three shapes by the direction and the size of the changes. Shape 1: sharp decrease but not to the baseline, Shape 2: sharp decrease to the baseline (absence), and Shape 3: reverse flow. RESULTS: At all hiccups, the changes on DBFW of these arteries were observed. These changes were classified into three shapes. Changes of umbilical artery were widely distributed in three shapes depending on when hiccup occurred during cardiac cycle. On the other hand, most changes of the descending aorta and middle cerebral artery were Shape 3 whenever the hiccup occurred during cardiac cycle. CONCLUSION: The changes on DBFW of fetal arteries were observed at all hiccups. Changes of umbilical artery were widely distributed in three shapes depending on when hiccup occurred during cardiac cycle. On the other hand, most changes of descending aorta and middle cerebral artery were Shape 3. This is the first study clarified the influence of fetal hiccups on DBFW of some fetal arteries, and showed the difference in these influences among fetal arteries.

Wearable Sensors for the Monitoring of Maternal Health-A Systematic Review.

Maternal health includes health during pregnancy and childbirth. Each stage during pregnancy should be a positive experience, ensuring that women and their babies reach their full potential in health and well-being. However, this cannot always be achieved. According to UNFPA (United Nations Population Fund), approximately 800 women die every day from avoidable causes related to pregnancy and childbirth, so it is important to monitor mother and fetal health throughout the pregnancy. Many wearable sensors and devices have been developed to monitor both fetal and the mother's health and physical activities and reduce risk during pregnancy. Some wearables monitor fetal ECG or heart rate and movement, while others focus on the mother's health and physical activities. This study presents a systematic review of these analyses. Twelve scientific articles were reviewed to address three research questions oriented to (1) sensors and method of data acquisition; (2) processing methods of the acquired data; and (3) detection of the activities or movements of the fetus or the mother. Based on these findings, we discuss how sensors can help effectively monitor maternal and fetal health during pregnancy. We have observed that most of the wearable sensors were used in a controlled environment. These sensors need more testing in free-living conditions and to be employed for continuous monitoring before being recommended for mass implementation.

Effects of cumulative duration of repeated anaesthesia exposure on foetal brain development in the ovine model.

OBJECTIVE: Anaesthesia is required in 0.4-1% of pregnant women, and prolonged and repeated exposures to anaesthesia may be required. It is unknown whether these exposures may result in foetal neurotoxicity in humans. As sheep have a gestation comparable to that of humans, the objective of this study was to analyse the neurodevelopmental outcome of ovine foetuses that had been exposed in utero to repeated and prolonged anaesthesia. DESIGN: Randomized controlled preclinical study. SETTING: Anaesthesia for non-obstetric surgery during pregnancy. ANIMALS: Twenty-four healthy pregnant Swifter ewes. INTERVENTIONS: The ewes were randomized to no anaesthesia exposure (control-group), single exposure (at gestational age 68-70 days), or repeated exposure (at gestational age 68-70 days and 96-98 days) to 2.5 h of sevoflurane anaesthesia and maternal laparotomy. All lambs were delivered at approximately term gestation (gestational age: 140-143 days). MEASUREMENTS: The primary outcome was neuron density in the frontal cortex 24 h after birth for the control-group versus the repeated-exposure-group. Key secondary outcome was the time needed to achieve the milestone of standing. Secondary outcomes included other neurobehavioural assessments (e.g., motoric milestones) and histological parameters quantified in multiple brain regions (neuron density, total cell density, proliferation, inflammation, synaptogenesis, astrocytes and myelination). MAIN RESULTS: Neuron density in the frontal cortex did not differ between groups (mean ±â€¯standard deviation: control-group: 403 ±â€¯39, single-exposure group: 436 ±â€¯23 and repeated-exposure-group: 403 ±â€¯40 neurons/mm2, control-group versus repeated-exposure-group: p = 0.986, control-group versus single-exposure-group: p = 0.097). No significant difference was observed for the time needed to achieve the milestone of standing. Only very limited differences were observed for other histological outcome parameters and neurobehavioural assessments. CONCLUSIONS: There is no evidence for foetal neuronal injury or neurobehavioural impairments after a cumulative duration of 5 h repetitive prenatal anaesthesia in sheep.

A Computational Procedure to Derive the Curve of Carus for Childbirth Computational Modeling.

Computational modeling serves an important role in childbirth-related research. Prescribed fetal descent trajectory is a key characteristic in childbirth simulations. Two major types of fully prescribed fetal descent trajectories can be identified in the literature: straight descent trajectories and curve of Carus. The straight descent trajectory has the advantage of being simpler and can serve as a reasonable approximation for relatively small fetal movements during labor, but it cannot be used to simulate the entire childbirth process. The curve of Carus is the well-recognized fetal descent trajectory with physiological significance. However, no detailed procedure to geometrically define the curve of Carus can be found in existing computational studies. This status of curve of Carus simulation in the literature hinders the direct comparison of results across different studies and the advancement of computational techniques built upon previous research. The goals of this study are: (1) propose a universal approach to derive the curve of Carus for the second stage of labor, from the point when the fetal head engages the pelvis to the point when the fetal head is fully delivered; and (2) demonstrate its utility when considering various fetal head sizes. The current study provides a detailed formulation of the curve of Carus, considering geometries of both the mother and the fetus. The maternal geometries were obtained from MRI data, and the fetal head geometries were based on laser scanning of a replica of a real fetal head.

Fetal defenses against intrapartum head compression-implications for intrapartum decelerations and hypoxic-ischemic injury.

Uterine contractions during labor and engagement of the fetus in the birth canal can compress the fetal head. Its impact on the fetus is unclear and still controversial. In this integrative physiological review, we highlight evidence that decelerations are uncommonly associated with fetal head compression. Next, the fetus has an impressive ability to adapt to increased intracranial pressure through activation of the intracranial baroreflex, such that fetal cerebral perfusion is well-maintained during labor, except in the setting of prolonged systemic hypoxemia leading to secondary cardiovascular compromise. Thus, when it occurs, fetal head compression is not necessarily benign but does not seem to be a common contributor to intrapartum decelerations. Finally, the intracranial baroreflex and the peripheral chemoreflex (the response to acute hypoxemia) have overlapping efferent effects. We propose the hypothesis that these reflexes may work synergistically to promote fetal adaptation to labor.

Feasibility study of an artificial placenta system consisting of a loop circuit configuration extracorporeal membrane oxygenation with a bridge circuit in the form of the umbilical arterial-venous connection.

We developed a new artificial placenta (AP) system consisting of a loop circuit configuration extracorporeal membrane oxygenation (ECMO) with a bridge circuit designed to be applied to the fetus in the form of an umbilical arterial-venous connection. We aimed to evaluate the feasibility of the AP system by performing a hydrodynamic simulation using a mechanical mock circulation system and fetal animal experiment. The effect of the working condition of the AP system on the fetal hemodynamics was evaluated by hydrodynamic simulation using a mechanical mock circulation system, assuming the weight of the fetus to be 2 kg. The AP system was introduced to two fetal goats at a gestational age of 135 days. The general conditions of the experimental animals were evaluated. The mock simulation showed that in an AP system with ECMO in the form of an umbilical arterial-venous connection in series, it could be difficult to maintain fetal hemodynamics when high ECMO flow was applied. The developed AP system could have high ECMO flow with less umbilical blood flow; however, the possibility of excessive load on the fetal right-sided heart should be noted. In the animal experiment, kid 1 (1.9 kg) was maintained on the AP system for 12 days and allowed to grow to term. In kid 2 (1.6 kg), the AP system could not be established because of the occlusion of the system by a thrombus. The developed AP system was feasible under both in vitro and in vivo conditions. Improvements in the AP system and management of the general fetal conditions are essential.

Blood Flow and Respiratory Gas Exchange in the Human Placenta at Term: A Data Update.

Reliable measurements using modern techniques and consensus in experimental design have enabled the assessment of novel data sets for normal maternal and foetal respiratory physiology at term. These data sets include (a) principal factors affecting placental gas transfer, e.g., maternal blood flow through the intervillous space (IVS) (500 mL/min) and foeto-placental blood flow (480 mL/min), and (b) O2, CO2 and pH levels in the materno-placental and foeto-placental circulation. According to these data, the foetus is adapted to hypoxaemic hypoxia. Despite flat oxygen partial pressure (pO2) gradients between the blood of the IVS and the umbilical arteries of the foetus, adequate O2 delivery to the foetus is maintained by the higher O2 affinity of the foetal blood, high foetal haemoglobin (HbF) concentrations, the Bohr effect, the double-Bohr effect, and high foeto-placental (=umbilical) blood flow. Again, despite flat gradients, adequate CO2 removal from the foetus is maintained by a high diffusion capacity, high foeto-placental blood flow, the Haldane effect, and the double-Haldane effect. Placental respiratory gas exchange is perfusion-limited, rather than diffusion-limited, i.e., O2 uptake depends on O2 delivery.

Fetal and Neonatal Anesthesia.

Anesthesia for fetal and neonatal surgery requires subspecialized knowledge and expertise. Attention to important anatomic, physiologic, and metabolic differences seen in pregnancy and at birth are essential for the optimal care of these patients. Thorough preoperative evaluations tailored intraoperative strategies and careful postoperative management are critical when devising the anesthetic approach for each of these cases.

Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach.

The non-invasive fetal electrocardiogram (FECG) derived from abdominal surface electrodes has been widely used for fetal heart rate (FHR) monitoring to assess fetal well-being. However, the accuracy of FECG-based FHR estimation heavily depends on the quality of FECG signal itself, which can generally be affected by several interference sources such as maternal heart activities and fetal movements. Hence, FECG signal quality assessment (SQA) is an essential task to improve the accuracy of FHR estimation by removing or interpolating low-quality FECG signals. In recent research, various SQA methods based on supervised learning have been proposed. Although these methods could perform accurate SQA, they require large labeled datasets. Nevertheless, the labeled datasets for the FECG SQA are very limited. In this paper, to address this limitation, we propose an unsupervised learning-based SQA method for identifying high and low-quality FECG signal segments. Specifically, a fully convolutional network (FCN)-based autoencoder (AE) is trained for reconstructing a spectrogram derived from FECG. An AE-based feature related to reconstruction error is then calculated to identify high and low-quality FECG segments. In addition, entropy-based features, statistical features, and ECG signal quality indices (SQIs) are also extracted. The high and low-quality segments are identified by feeding the extracted features into self-organizing map (SOM). The experimental results showed that our proposal achieved an accuracy of 98% in high and low-quality signal classification.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording.

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus's health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.