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Investigate racial and ethnic differences in infant sleep and examine associations with insurance status and parent-infant bedtime behavioral factors (PIBBF).Participants are part of the COVID-19 Mother Baby Outcomes (COMBO) Initiative, Columbia University. Data on infant sleep (night, day and overall sleep duration, night awakenings, latency, infant's sleep as a problem) were collected at 4 months postpartum. Regressions estimated associations between race/ethnicity, insurance status, PIBBF and infants' sleep.A total of 296 infants were eligible (34.4% non-Hispanic White [NHW], 10.1% Black/African American [B/AA], 55.4% Hispanic). B/AA and Hispanic mothers were more likely to have Medicaid, bed/room-share, and report later infant bedtime compared to NHW mothers. Infants of B/AA mothers had longer sleep latency compared to NHW. Infants of Hispanic mothers slept less at night (∼70 ± 12 minutes) and more during the day (∼41 ± 12 minutes) and Hispanic mothers were less likely to consider infants' sleep as a problem compared to NHW (odds ratio 0.4; 95% confidence interval: 0.2-0.7). After adjustment for insurance status and PIBBF, differences by race/ethnicity for night and day sleep duration and perception of infant's sleep as a problem persisted (∼32 ± 14 minutes, 35 ± 15 minutes, and odds ratio 0.4; 95% confidence interval: 0.2-0.8 respectively). Later bedtime was associated with less sleep at night (∼21 ± 4 minutes) and overall (∼17 ± 5 minutes), and longer latency. Infants who did not fall asleep independently had longer sleep latency, and co-sleeping infants had more night awakenings.Results show racial/ethnic differences in sleep in 4-month-old infants across sleep domains. The findings of our study suggest that PIBBF have an essential role in healthy infant sleep, but they may not be equitably experienced across racial/ethnic groups.

Serotonin shapes brain structure and function during early development across phylogenetically diverse species. In mice and humans, perinatal SSRI exposure produces brain alterations and increases anxiety/depression-related behaviors in the offspring. It remains unclear whether shared brain circuit changes underlie the behavioral impact of perinatal SSRIs across species. We examine how developmental SSRI-exposure in mice and humans changes fear-related brain activation and behavior. SSRI-administered mice showed increased defense responses to a predator odor that were associated with stronger fMRI-based fear circuit activation when compared to saline controls. Similarly, human adolescents exposed to SSRIsin uteroshowed greater activation of fear brain structures and exhibited higher anxiety and depressive symptoms than unexposed adolescents. Perinatal SSRI enhances innate fear-related responses and fear brain circuit activation that are conserved across species.One Sentence SummarySince SSRI use in pregnancy is common, we determined the effects of altered serotonin signaling during development in mice and humans.

Prenatal smoking and drinking are associated with sudden infant death syndrome and neurodevelopmental disorders. Infants with these outcomes also have altered autonomic nervous system (ANS) regulation. We examined the effects of prenatal smoking and drinking on newborn ANS function.Pregnant women were enrolled in Northern Plains, USA (NP) and Cape Town (CT), South Africa. Daily drinking and weekly smoking data were collected prenatally. Physiological measures were obtained during sleep 12-96 h post-delivery.In all, 2913 infants from NP and 4072 from CT were included. In active sleep, newborns of mothers who smoked throughout pregnancy, compared to non-smokers, had higher breathing rates (2.2 breaths/min; 95% CI: 0.95, 3.49). Quit-early smoking was associated with reductions in beat-to-beat heart rate variability (HRV) in active (-0.08 s) and quiet sleep (-0.11 s) in CT. In girls, moderate-high continuous smoking was associated with increased systolic (3.0 mmHg, CI: 0.70, 5.24) and diastolic blood pressure (2.9 mmHg, CI: 0.72, 5.02). In quiet sleep, low-continuous drinking was associated with slower heart rate (-4.5 beat/min). In boys, low-continuous drinking was associated with a reduced ratio of low-to-high frequency HRV (-0.11, CI: -0.21, -0.02).These findings highlight potential ANS pathways through which prenatal drinking and smoking may contribute to neurodevelopment outcomes.In this prospective cohort study of 6985 mother-infant dyads prenatal drinking and smoking were associated with multiple ANS parameters. Smoking was associated with increased neonatal breathing rates among all infants, and heart rate variability (HRV) and blood pressure (BP) among girls. Drinking was associated with reductions in HR and BP among all newborns, and reductions in the ratio of low to-high frequency HRV among boys. These findings suggest that prenatal smoking and drinking alter newborn ANS which may presage future neurodevelopmental disorders.

To investigate the use of computerized cardiotocography (cCTG) parameters in Intrauterine Growth Restriction (IUGR) pregnancies for the prediction of 1) complication with preeclampsia; 2) placental histological abnormalities, and 3) neonatal outcomes. A single-center observational retrospective case-control study was performed by reviewing medical records, cCTG databases and pathological reports of women with singleton pregnancy and IUGR uncomplicated (controls) and complicated by preeclampsia (cases). Primary endpoint was the association between cCTG parameters and preeclampsia in IUGR. Secondary endpoints were the association between cCTG parameters and 1) placental abnormalities, and 2) neonatal outcomes. The one-way ANOVA test was used to compare cCTG parameters in cases and controls. t-test was applied to compare neonatal outcomes and placental abnormalities in both groups. The Spearman Test value Correlation coefficients between the cCTG parameters and neonatal outcome in the two groups. A p value < .05 was considered significant for all analyses. Among all cCTG parameters, a significant association with preeclampsia in IUGR was found for Fetal Heart Rate (FHR, p = .008), Delta (p = .018), Short Term Variability (STV, p = .021), Long Term Variability (LTV, p = .028), Acceleration Phase Rectified Slope (APRS, p = .018) and Deceleration Phase Rectified Slope (DPRS, p = .038). Of all placental histologic abnormalities, only vascular alterations at least moderate were significantly associated with increased FHR (p = .02). About neonatal outcomes, all cCTG parameters were significantly associated with birth weight, Apgar index at 1 and 5 min, pH and pCO2. FHR, LTI, Delta, Approximate Entropy (ApEn) and LF were significantly associated with pO2; LTI, Interval Index (II) and ApEn with base excess. Among controls, Delta, ApEn, Low Frequency (LF) and High Frequency (HF) were significantly associated with pCO2, while among cases, STV and Delta were significantly associated with pH; STV, LTI, Delta, ApEn, LF and HF with pCO2; STV, LTI, Delta and ApEn with pO2; HF with base excess; FHR and LF with lactates. cCTG parameters may be useful to detect complication with preeclampsia in IUGR pregnancies. Regarding placental status, cCTG parameters may detect overall circulation alterations, but not specific histological abnormalities. Lastly, all cCTG parameters may predict neonatal outcomes, helping to tailor the patients’ management.

This study is the first to examine spectrum-wide (1 to 250 Hz) differences in electroencephalogram (EEG) power between eyes open (EO) and eyes closed (EC) resting state conditions in 486 children. The results extend the findings of previous studies by characterizing EEG power differences from 30 to 250 Hz between EO and EC across childhood. Developmental changes in EEG power showed spatial and frequency band differences as a function of age and EO/EC condition. A 64-electrode system was used to record EEG at 4, 5, 7, 9, and 11 years of age. Specific findings were: (1) the alpha peak shifts from 8 Hz at 4 years to 9 Hz at 11 years, (2) EC results in increased EEG power (compared to EO) at lower frequencies but decreased EEG power at higher frequencies for all ages, (3) the EEG power difference between EO and EC changes from positive to negative within a narrow frequency band which shifts toward higher frequencies with age, from 9 to 12 Hz at 4 years to 32 Hz at 11 years, (4) at all ages EC is characterized by an increase in lower frequency EEG power most prominently over posterior regions, (5) at all ages, during EC, decreases in EEG power above 30 Hz are mostly over anterior regions of the scalp. This report demonstrates that the simple challenge of opening and closing the eyes offers the potential to provide quantitative biomarkers of phenotypic variation in brain maturation by employing a brief, minimally invasive protocol throughout childhood.

Parental input shapes youth self-regulation development, and a lack of sensitive caregiving is a risk factor for youth mental health problems. Parents may shape youth regulation through their influence over biological (including neural) and behavioral development during childhood at both micro (moment-to-moment) and macro (global) levels. Prior studies have shown that micro-level parent-child interactions shape youth's biology contributing to youth mental health. However, it remains unclear whether prior disrupted/absent care affects those moment-to-moment parent-youth interactions in ways that increase risk for youth psychopathology. In the current study, we calculated transfer entropy on cardiac data from parent-youth dyads where the youth had either been exposed to disrupted care prior to adoption or not. Transfer Entropy (TE) tracks information flow between two signals, enhancing quantification of directional coupling, allowing for the examination of parent-child and child-parent influences. Using this novel approach, we identified lower cardiac information transfer from youth-to-parents in dyads where the youth had been exposed to disrupted/absent early care. Moreover we showed that the degree to which the parent's physiology changed in response to youth's physiology was negatively related to the youth's mental health, representing a potential pathway for elevated mental health risk in populations exposed to disrupted/absent early care.

Prenatal exposures to alcohol (PAE) and tobacco (PTE) are known to produce adverse neonatal and childhood outcomes including damage to the developing auditory system. Knowledge of the timing, extent, and combinations of these exposures on effects on the developing system is limited. As part of the physiological measurements from the Safe Passage Study, Auditory Brainstem Responses (ABRs) and Transient Otoacoustic Emissions (TEOAEs) were acquired on infants at birth and one-month of age. Research sites were in South Africa and the Northern Plains of the U.S. Prenatal information on alcohol and tobacco exposure was gathered prospectively on mother/infant dyads. Cluster analysis was used to characterize three levels of PAE and three levels of PTE. Repeated-measures ANOVAs were conducted for newborn and one-month-old infants for ABR peak latencies and amplitudes and TEOAE levels and signal-to-noise ratios. Analyses controlled for hours of life at test, gestational age at birth, sex, site, and other exposure. Significant main effects of PTE included reduced newborn ABR latencies from both ears. PTE also resulted in a significant reduction of ABR peak amplitudes elicited in infants at 1-month of age. PAE led to a reduction of TEOAE amplitude for 1-month-old infants but only in the left ear. Results indicate that PAE and PTE lead to early disruption of peripheral, brainstem, and cortical development and neuronal pathways of the auditory system, including the olivocochlear pathway.

Approximately 7% of preterm infants receive an autism spectrum disorder (ASD) diagnosis. Yet, there is a significant gap in the literature in identifying prospective markers of neurodevelopmental risk in preterm infants. The present study examined two electroencephalography (EEG) parameters during infancy, absolute EEG power and aperiodic activity of the power spectral density (PSD) slope, in association with subsequent autism risk and cognitive ability in a diverse cohort of children born preterm in South Africa. Participants were 71 preterm infants born between 25 and 36 weeks gestation (34.60 ± 2.34 weeks). EEG was collected during sleep between 39 and 41 weeks postmenstrual age adjusted (40.00 ± 0.42 weeks). The Bayley Scales of Infant Development and Brief Infant Toddler Social Emotional Assessment (BITSEA) were administered at approximately 3 years of age adjusted (34 ± 2.7 months). Aperiodic activity, but not the rhythmic oscillatory activity, at multiple electrode sites was associated with subsequent increased autism risk on the BITSEA at three years of age. No associations were found between the PSD slope or absolute EEG power and cognitive development. Our findings highlight the need to examine potential markers of subsequent autism risk in high-risk populations other than infants at familial risk.

Study ObjectivesValidate a HR-based deep-learning algorithm for sleep staging named Neurobit-HRV (Neurobit Inc., New York, USA).MethodsThe algorithm can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4-levels (Wake; Light; Deep; REM) in 30-second epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n=994 participants) and a proprietary dataset (Z3Pulse, n=52 participants), composed of HR recordings collected with a chest-worn, wireless sensor. A simultaneous PSG was collected using SOMNOtouch. We evaluated the performance of the models in both datasets using Accuracy (A), Cohen’s kappa (K), Sensitivity (SE), Specificity (SP).ResultsCinC - The highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect sleep scoring, while a significant decrease of performance by age was reported across the models. Z3Pulse - The highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment.ConclusionsResults demonstrate the feasibility of accurate HR-based sleep staging. The combination of the illustrated sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution easily deployable in the home.

This paper aims to problematize the issue of reuse and reoccupation of architectures and building materials in the Near East. So far, the vast majority of research and published work dealing with this topic (not only in the field of Islamic Archaeology and Art History) have focused on the monumental complexes of urban centres. In this framework, the concept of spolia has been at the centre of a long and still heated transdisciplinary debate. Vernacular architecture and rural contexts have, for the most part, been neglected. Frequent episodes of reoccupation of earlier structures, even if thoroughly described in archaeological reports are almost automatically branded as the result of pragmatic behaviour of local communities benefitting from the availability of abundant building materials from ruined structures. However, the vast number of ways in which reuse and reoccupation might have occurred is often overlooked. Even conceding that most of the evidence is likely due to some form or other of “pragmatism,” the different ways in which these appear need to be more fully explained and interpreted. This paper, which builds on existing scholarship of reuse and reoccupation, argues for a rethinking of the methodology. Other experiences, most notably those investigating the late Antique and early Medieval western Mediterranean, provide a useful point of reference for where to start the discussion. This paper will demonstrate how extending the perspective from the single building to the broader context, including the surrounding landscape, is ultimately the only way to fully comprehend the archaeological evidence and possibly better understand and explain the different “fates” of architectures.

Background The clinical diagnosis of late Fetal Growth Restriction (FGR) involves the integration of Doppler ultrasound data and Fetal Heart Rate (FHR) monitoring through computer assisted computerized cardiotocography (cCTG). The aim of the study was to evaluate the diagnostic power of combined Doppler and cCTG parameters by contrasting late FGR –and healthy controls. Methods The study was conducted from January 2018 to May 2020. Only pregnant women who had the last Doppler measurement obtained within 1 week before delivery and cCTG performed within 24 h before delivery were included in the study. Two hundred forty-nine pregnant women fulfilling the inclusion criteria were enrolled in the study; 95 were confirmed as late FGR and 154 were included in the control group. Results Among the extracted cCTG parameters, Delta Index, Short Term Variability (STV), Long Term Variability (LTV), Acceleration and Deceleration Phase Rectified Slope (APRS, DPRS) values were lower in the late FGR participants compared to the control group. In the FGR cohort, Delta, STV, APRS, and DPRS were found different when stratifying by MCA_PI (MCA_PI 5th centile). STV and DPRS were the only parameters to be found different when stratifying by (UA_PI >95th centile or UA_PI The AUC of ROC curves were accurate for STV (0,70), Delta (0,68), APRS (0,65) and DPRS (0,71) when UA_PI values were > 95th centile while, the accuracy attributable to the prediction of MCA_PI was 0.76, 0.77, 0.73, and 0.76 for STV, Delta, APRS, and DPRS, respectively. An association of UA_PI>95th centile and MCA_PI Conclusions The results of this study show the contribution of advanced cCTG parameters and fetal Doppler to the identification of late FGR and the association of those parameters with the risk for NICU admission. Trial registration Retrospectively registered.

Objective Trophoblast inclusions—cross sections of abnormal trophoblast bilayer infoldings—have previously been associated with aneuploidy, placenta accreta, and prematurity. This study was conducted to establish the relationship between trophoblast inclusions and a range of placental, pregnancy, and birth outcomes in a patient population with high smoking and alcohol exposure. Specifically, we sought to evaluate the association between the presence of trophoblast inclusions and 1) three primary birth outcomes: full-term birth, preterm birth, and stillbirth; 2) gestational age at delivery; and 3) specific placental pathologies. Methods Two slides containing chorionic villi were evaluated from 589 placentas that were collected from Stellenbosch University in Cape Town, South Africa as part of the prospective, multicenter cohort Safe Passage Study of the Prenatal Alcohol and SIDS and Stillbirth Network. The subsample included 307 full-term live births, 212 preterm live births, and 70 stillbirths. Results We found that the odds of identifying at least one trophoblast inclusion across two slides of chorionic villi was significantly higher for placentas from preterm compared to term liveborn deliveries (OR = 1.74; 95% CI: 1.22, 2.49, p = 0.002), with an even greater odds ratio for placentas from stillborn compared to term liveborn deliveries (OR = 4.95; 95% CI: 2.78, 8.80, p < 0.001). Gestational age at delivery was inversely associated with trophoblast inclusion frequency. Trophoblast inclusions were significantly associated with small for gestational age birthweight, induction of labor, villous edema, placental infarction, and inflammation of the chorionic plate. Conclusions The novel associations that we report warrant further investigation in order to understand the complex network of biological mechanisms through which the factors that lead to trophoblast inclusions may influence or reflect the trajectory and health of a pregnancy. Ultimately, this line of research may provide critical insights that could inform both clinical and research applications.

Key Points Question Is prenatal exposure to maternal drinking and smoking associated with the risk of stillbirth? Findings In this cohort study of 8506 pregnant women (with 11 892 pregnancies) in Cape Town, South Africa, and the Northern Plains in the US, dual exposure to drinking and smoking after the first trimester of pregnancy had 2.78 times the risk of late stillbirth compared with those with no exposure or who had quit before the end of the first trimester of pregnancy. Meaning These findings suggest that dual exposure to drinking and smoking after the first trimester of pregnancy is associated with nearly 3 times the risk of late stillbirth., Importance Prenatal smoking is a known modifiable risk factor for stillbirth; however, the contribution of prenatal drinking or the combination of smoking and drinking is uncertain. Objective To examine whether prenatal exposure to alcohol and tobacco cigarettes is associated with the risk of stillbirth. Design, Setting, and Participants The Safe Passage Study was a longitudinal, prospective cohort study with data collection conducted between August 1, 2007, and January 31, 2015. Pregnant women from Cape Town, South Africa, and the Northern Plains region of the US were recruited and followed up throughout pregnancy. Data analysis was performed from November 1, 2018, to November 20, 2020. Exposure Maternal consumption of alcohol and tobacco cigarettes in the prenatal period. Main Outcomes and Measures The main outcomes were stillbirth, defined as fetal death at 20 or more weeks’ gestation, and late stillbirth, defined as fetal death at 28 or more weeks' gestation. Self-reported alcohol and tobacco cigarette consumption was captured at the recruitment interview and up to 3 scheduled visits during pregnancy. Participants were followed up during pregnancy to obtain delivery outcome. Results Of 11663 pregnancies (mean [SD] gestational age at enrollment, 18.6 [6.6] weeks) in 8506 women for whom the pregnancy outcome was known by 20 weeks’ gestation or later and who did not terminate their pregnancies, there were 145 stillbirths (12.4 per 1000 pregnancies) and 82 late stillbirths (7.1 per 1000 pregnancies). A total of 59% of pregnancies were in women from South Africa, 59% were in multiracial women, 23% were in White women, 17% were in American Indian women, and 0.9% were in women of other races. A total of 8% were older than 35 years. In 51% of pregnancies, women reported no alcohol or tobacco cigarette exposure (risk of stillbirth, 4 per 1000 pregnancies). After the first trimester, 18% drank and smoked (risk of stillbirth, 15 per 1000 births), 9% drank only (risk of stillbirth, 10 per 1000 pregnancies), and 22% smoked only (risk of stillbirth, 8 per 1000 pregnancies). Compared with the reference group (pregnancies not prenatally exposed or without any exposure after the first trimester), the adjusted relative risk of late stillbirth was 2.78 (98.3% CI, 1.12-6.67) for pregnancies prenatally exposed to drinking and smoking, 2.22 (98.3% CI, 0.78-6.18) for pregnancies prenatally exposed to drinking only after the first trimester, and 1.60 (98.3% CI, 0.64-3.98) for pregnancies prenatally exposed to smoking only after the first trimester. The adjusted relative risk for all stillbirths was 1.75 (98.3% CI, 0.96-3.18) for dual exposure, 1.26 (98.3% CI, 0.58-2.74) for drinking only, and 1.27 (98.3% CI, 0.69-2.35) for smoking only compared with the reference group. Conclusions and Relevance These results suggest that combined drinking and smoking after the first trimester of pregnancy, compared with no exposure or quitting before the end of the first trimester, may be associated with a significantly increased risk of late stillbirth., This cohort study assesses whether prenatal exposure to alcohol, tobacco cigarettes, or both is associated with the risk of stillbirth in pregnant women from Cape Town, South Africa, and the Northern Plains region of the US.

Objective/Design Cross-sectional study to examine the determinants of sleep health among postpartum women during the COVID-19 pandemic in New York City (NYC). Setting/Participants A subset of participants recruited as part of the COVID-19 Mother Baby Outcomes (COMBO) cohort at Columbia University (N = 62 non-Hispanic White, N = 17 African American, N = 107 Hispanic). Measurements Data on maternal sleep, COVID-19 infection during pregnancy, sociodemographic, behavioral, and psychological factors were collected via questionnaire at 4 months postpartum. Self-reported subjective sleep quality, latency, duration, efficiency, disturbances, and daytime dysfunction were examined as categorical variables (Pittsburgh Sleep Quality Index [PSQI]). Associations between sleep variables and COVID-19 status, time of the pandemic, sociodemographic, behavioral, and psychological factors were estimated via independent multivariable regressions. Results Mothers who delivered between May-December 2020, who delivered after the NYC COVID-19 peak, experienced worse sleep latency, disturbances and global sleep health compared to those who delivered March-April 2020, the peak of the pandemic. Maternal depression, stress and COVID-19-related post-traumatic stress were associated with all sleep domains except for sleep efficiency. Maternal perception of infant's sleep as a problem was associated with worse global PSQI score, subjective sleep quality, duration, and efficiency. Compared to non-Hispanic White, Hispanic mothers reported worse global PSQI scores, sleep latency, duration and efficiency, but less daytime dysfunction. Conclusions These findings provide crucial information about sociodemographic, behavioral, and psychological factors contributing to sleep health in the postpartum period.

Study ObjectivesTo quantify the association between race/ethnicity and maternal and infant self-reported sleep health at 4 months, exploring the role of maternal depression, stress and symptoms of trauma related to the COVID-19 pandemic as potential mediators.MethodsParticipants were recruited as part of the COVID-19 Mother Baby Outcomes (COMBO) cohort at Columbia University (N=71 non-Hispanic White, N=14 African American (AA), N=113 Hispanic, N=40 other/declined). Data on infant sleep were collected at 4 months postpartum. A subset of 149 women also completed questionnaires assessing maternal mental health and sleep. Multivariable regressions were used to separately estimate associations of race/ethnicity and mental health with multiple sleep domains for infants and mothers adjusting for individual-level covariates.ResultsCompared to non-Hispanic White, Hispanic infants slept less at night (β=- 101.7±17.6, pConclusionsRacial/ethnic disparities in maternal and infant sleep are observable at 4 months post-partum. Maternal stress, depression and symptoms of trauma related to the COVID-19 pandemic did not mediate these associations.

Background — Missing data are a source of bias in many epidemiologic studies. This is problematic in alcohol research where data missingness may not be random as they depend on patterns of drinking behavior. Methods — The Safe Passage Study was a prospective investigation of prenatal alcohol consumption and fetal/infant outcomes (n=11,083). Daily alcohol consumption for the last reported drinking day and 30 days prior was recorded using the Timeline Followback method. Of 3.2 million person-days, data were missing for 0.36 million. We imputed missing exposure data using a machine learning algorithm; “K Nearest Neighbor” (K-NN). K-NN imputes missing values for a participant using data of other participants closest to it. Since participants with no missing days may not be comparable to those with missing data, segments from those with complete and incomplete data were included as a reference. Imputed values were weighted for the distances from nearest neighbors and matched for day of week. We validated our approach by randomly deleting non-missing data for 5-15 consecutive days. Results — We found that data from 5 nearest neighbors (i.e. K=5) and segments of 55 days provided imputed values with least imputation error. After deleting data segments from a first trimester data set with no missing days, there was no difference between actual and predicted values for 64% of deleted segments. For 31% of the segments, imputed data were within +/-1 drink/day of the actual. Conclusions — K-NN can be used to impute missing data in longitudinal studies of alcohol use during pregnancy with high accuracy.

BACKGROUND The prevalence of gestational diabetes mellitus (GDM) has rapidly increased, yet few prior studies have investigated parameters of early brain development in infants born to gestational diabetic mothers. The present study assessed visual evoked potentials (VEPs) in healthy infants born to gestational diabetic mothers and matched controls. METHODS After exclusions, in this prospective study we examined VEPs in 73 neonates between 37 weeks and 41 weeks gestation at birth (n = 37 infants of gestational diabetic mothers). Stroboscopic flashes were presented through closed eyelids during passive electroencephalography (EEG) recording to derive VEP waveforms during natural sleep. RESULTS There was a statistically significant moderate correlation between gestational age at birth and P2 latency of the flash VEP where P2 latency significantly decreased with increasing gestational age (Pearson's R(73) = -0.32, p

The prognostic ability of Fetal Heart Rate (FHR) analysis by means of Phase Rectified Signal Averaging (PRSA) has resulted successful in the context of Intrauterine Growth Restriction (IUGR) diagnosis. Nevertheless, there is a lack of consensus among the various implementation of the methodology and parameter setting (s, T, L), depending on the insurgence and progression of the pathology. In this work, we provide evidence for the discriminative performance of PRSA-derived indices over different combinations of PRSA parameters in a population of late IUGR fetuses.

The importance of fetal surveillance during pregnancy is worldwide accepted since its peculiar ability to anticipate fetal distress under a variety of conditions. The novel frontier in the field of remote fetal monitoring relies on a continuous and everyday-monitoring of fetal wellbeing. As a consequence, fECG monitoring systems have seen a net increase in popularity in the recent years. In this paper, we propose a novel algorithm for the detection of fECG and we validated its performances by testing it on an open source collection of 75 annotated fECG traces. Our results show the reliability of the proposed methodology in extracting fECG and deriving an estimate of fHR.

Characterization of patterns of alcohol consumption during pregnancy encompasses multiple factors such as magnitude, frequency, and timing of exposure throughout gestation. Traditional statistical models are limited in dealing with multivariate and diverse patterns of exposure as in the context of this analysis. We propose a finite mixture model-based approach to derive clusters of alcohol exposure of participants in the Safe Passage Study (PASS). Daily alcohol consumption data for 11,083 pregnant women have been clustered in 10 different exposed groups. The resulting cluster analysis was able to characterize alcohol consumption in a comprehensive framework capable of taking into account both quantity and timing of exposure as well as the occurrence of binge drinking.

Background and objectives Intrauterine Growth Restriction (IUGR) is a fetal condition defined as the abnormal rate of fetal growth. The pathology is a documented cause of fetal and neonatal morbidity and mortality. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. Therefore, designing an accurate model for the early and prompt identification of pathology in the antepartum period is crucial in view of pregnancy management. Methods We tested the performance of 15 machine learning techniques in discriminating healthy versus IUGR fetuses. The various models were trained with a set of 12 physiology based heart rate features extracted from a single antepartum CardioTocographic (CTG) recording. The reason for the utilization of time, frequency, and nonlinear indices is based on their standalone documented ability to describe several physiological and pathological fetal conditions. Results We validated our approach on a database of 60 healthy and 60 IUGR fetuses. The machine learning methodology achieving the best performance was Random Forests. Specifically, we obtained a mean classification accuracy of 0.911 [0.860, 0.961 (0.95 confidence interval)] averaged over 10 test sets (10 Fold Cross Validation). Similar results were provided by Classification Trees, Logistic Regression, and Support Vector Machines. A features ranking procedure highlighted that nonlinear indices showed the highest capability to discriminate between the considered fetal conditions. Nevertheless, is the combination of features investigating CTG signal in different domains, that contributes to an increase in classification accuracy. Conclusions We provided validation of an accurate artificially intelligence framework for the diagnosis of IUGR condition in the antepartum period. The employed physiology based heart rate features constitute an interpretable link between the machine learning results and the quantitative estimators of fetal wellbeing.

In the fetal period, the progressive coordination among several subsystems promotes the emergence of sleep states. For this reason, the characterization of fetal behavioral states plays a crucial role in assessing fetal wellbeing. Nevertheless, current methodologies aimed at assessing fetal sleep states over limited time intervals require visual observation of the traces. In this work, we validate a point process approach for a continuous in time characterization of fetal behavioral states. We compare traditional heart rate variability (HRV) parameters and the corresponding point process-extracted sets of time and frequency measures in a population of 39 fetuses whose fetal ECG was recorded overnight during the third trimester of gestation.Clinical Relevance- Our results provide evidence for the proposed point process framework to capture fetal HRV dynamics with a high degree of reliability, suggesting its potential application for instantaneous estimates of fetal sleep states.

ICT4MOMs project aims at implementing a novel remote ICT service towards the monitoring and prediction of maternal and fetal conditions throughout pregnancy. The envisioned application is based on the integration of wearable sensors and devices connected by an ad-hoc smartphone app in communication with an ob-gyn clinical center. Advanced signal and image processing software tools will be developed for extracting information from the recorded signals, namely: fetal heart rate, uterine contractions, continuous glucose sensors and portable US probe. Once validated by the clinical partners, the collected dataset will be used for a multivariate analysis based on soft computing classifiers and machine learning techniques. Based on the growing literature providing evidence on the fact that mother-fetus system should be considered as a whole, in this proposal pregnancy is conceptualized as a continuously evolving system which needs to be investigated by means of time-varying approaches. The crucial expected outcome is the integration of the established clinical knowledge with the results of computational analysis. Such multilevel integration is expected to provide reliable and translatable clinical guidelines towards a novel pregnancy management encompassing a more inclusive monitoring framework designed on a patient-specific level. The project was recently funded by the Italian Government—Progetti di Interesse Nazionale (PRIN) under the grant number 2017RR5EW3 for the duration of three years (2019–2021).

Importance Research to date has not determined a safe level of alcohol or tobacco use during pregnancy. Electroencephalography (EEG) is a noninvasive measure of cortical function that has previously been used to examine effects of in utero exposures and associations with neurodevelopment. Objective To examine the association of prenatal exposure to alcohol (PAE) and tobacco smoking (PTE) with brain activity in newborns. Design, Setting, and Participants This prospective cohort study enrolled mother-newborn dyads from December 2011 through August 2015, with data analyzed from June 2018 through June 2019. Pregnant women were recruited from clinical sites in Cape Town, South Africa, and the Northern Plains region of the US. Participants were a subset of newborns enrolled in the Safe Passage Study. Exclusions included birth at less than 37 or more than 41 weeks’ gestation, multiple birth, or maternal use of psychiatric medication during pregnancy. Exposures PAE and PTE groups were determined by cluster analysis. Main Outcomes and Measures Analyses of covariance were run on EEG spectral power at 12 scalp locations across the frequency spectrum from 1 to 45 Hz in 3-Hz bins by sleep state. Results The final sample consisted of 1739 newborns (median [interquartile range] gestational age at birth, 39.29 [1.57] weeks; 886 [50.9%] were female; median [interquartile range] newborn age at assessment, 48.53 [44.96] hours). Newborns whose mothers were in the low continuous (95% CI, −0.379 to −0.031;P Conclusions and Relevance These findings suggest that even low levels of PAE or PTE are associated with changes in offspring brain development.

Introduction Sleep and mental health have a bidirectional relationship. During pregnancy, poor sleep health, depression and stress are common and have been associated with poor maternal and fetal outcomes. The COVID-19 pandemic has introduced additional physical and psychological risk factors, due to high mortality rate, and economic and social repercussions. This study examines whether prenatal maternal mental health clusters are associated with multiple dimensions of sleep during pregnancy in the context of the COVID-19 pandemic. Methods From June-December 2020, participants were recruited as part of the COVID-19 Mother Baby Outcomes (COMBO) Cohort at Columbia University (N=188; at recruitment gestational age: 32.2±8.2 weeks; age: 32±6.75 years; N=74 Hispanic, N=65 White non-Hispanic, N=27 Black/African American, N=22 other). Survey data on maternal depression (PHQ-9), perceived stress (PSS), Covid-related stress, and sleep health (PSQI) were collected. Using hierarchical clustering, we created maternal mental health clusters (MMHC). Regressions analyses were implemented to estimate the associations between multiple dimensions of sleep based on MMHC. Results \We derived three MMHC: Low-risk (no depression, no Covid-stress, low-moderate perceived stress), Covid-stress (no depression, moderate Covid-stress, low-moderate perceived stress) and high-risk (moderate depression, moderate Covid-stress, moderate to high perceived stress). Maternal age, gestational age, income, and race were not significantly different across clusters. The Covid-stress cluster compared to the low-risk cluster reported worse subjective sleep quality (ß=0.34±0.11, p=0.0025), longer sleep latency (ß=0.44±0.13,p Conclusion Our results indicate that the COVID-19 pandemic has affected mental health profiles during pregnancy, with evidence of a high-risk cluster presenting Covid-stress and depressive symptoms and a Covid-stress cluster presenting Covid-stress without depressive symptoms in a multi-ethnic sample of pregnant women. Both were associated with poorer sleep health outcomes compared to the low-risk cluster. These results have important implications for screening and treatment for the sleep health and obstetric communities during these unprecedented times. Support (if any)