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Background: Maternal and child mortality rates remain a serious public health challenge in Ethiopia. Short interpregnancy intervals are known to negatively impact perinatal and child health; therefore, improving this practice is a key strategy to reduce mortality and adverse outcomes. Objective: This study aimed to evaluate the extent of short intervals between pregnancies and the factors associated with them among women who gave birth at public hospitals in the North Wollo Zone of northeastern Ethiopia in 2023. Design: A facility-based cross-sectional study was conducted in the North Wollo Zone of northeastern Ethiopia in 2023. Methods: This study was conducted on mothers who delivered at public hospitals in the North Wollo Zone of northeastern Ethiopia. The study participants were selected using a systematic random sampling technique between February 1, 2023, and April 1, 2023. Data were collected through an interviewer-administered questionnaire using the Kobo Collect tool and analyzed using SPSS version 27.0. Bivariable and multivariable analyses were performed to determine the relationship between the outcome and predictor variables. A p value of less than 0.05 was considered significant in the multivariable analysis. Results: The study found that 51.5% of participants had short interpregnancy intervals. Several independent factors were associated with a short birth interval, including the status of the last pregnancy (adjusted odds ratio (AOR): 9.51; 95% CI: 3.932–23.0028), educational status of the woman (AOR: 4.545; 95% CI: 1.208–17.108), and use of modern contraceptives in previous pregnancies (AOR: 6.460, 95% CI: 3.882–23.008). Conclusion: Despite the efforts made through public health interventions in Ethiopia, the prevalence of short interpregnancy intervals in this study was high. Therefore, increasing the utilization of modern contraceptives and promoting health education at both health facilities and community levels could contribute to addressing this issue. Plain language summary: The study of interpregnancy intervals was conducted to better understand the magnitude and causes of short interpregnancy intervals among women who delivered at North Wollo Zone public hospitals Why was the study done? This study was conducted to evaluate the prevalence of short intervals between pregnancies and identify the factors associated with them among women who gave birth at public hospitals in the North Wollo Zone of northeastern Ethiopia in 2023. This research aimed to understand the extent of the issue and its determinants to inform strategies and interventions aimed at reducing maternal and child mortality and improving health outcomes in the region. What did the researchers do? This cross-sectional study was conducted in 2023 in the North Wollo Zone of northeastern Ethiopia. They selected mothers who had given birth at public hospitals between February and April 2023 using systematic random sampling. Data were collected through interviews and analyzed to identify factors associated with short birth intervals. What did the researchers find? The researchers found that over half (51.5%) of the participants had short intervals between pregnancies. They discovered significant associations between short birth intervals and factors such as the outcome of the last pregnancy, educational status of the women, and use of modern contraceptives in previous pregnancies. These findings highlight the importance of addressing these factors to improve the maternal and child health outcomes in the region. What do the findings mean? These findings suggest that a considerable proportion of women in the North Wollo Zone of northeastern Ethiopia experienced short intervals between pregnancies. Factors such as the outcome of previous pregnancies, educational status, and use of modern contraceptives play crucial roles in determining the length of birth intervals. Addressing these factors through improved access to modern contraceptives and enhanced health education could potentially reduce the maternal and child health risks associated with short birth intervals in the region. [ABSTRACT FROM AUTHOR]

Background: Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, and preterm birth. This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines. Methods: We analyzed data from the Araraquara Cohort, Brazil, which comprised 1557 pregnant women with a gestational age of 19 weeks or less. Predictors included socioeconomic, demographic, lifestyle, morbidity, and anthropometric factors. Five machine learning algorithms (Random Forest, LightGBM, AdaBoost, CatBoost, and XGBoost) were employed for model development. The models were trained and evaluated using a multiclass classification approach. Model performance was assessed using metrics such as area under the ROC curve (AUC-ROC), F1 score and Matthew's correlation coefficient (MCC). Results: The outcomes were categorized as follows: GWG within recommendations (28.7%), GWG below (32.5%), and GWG above recommendations (38.7%). The XGBoost presented the best overall model, achieving an AUC-ROC of 0.79 for GWG within, 0.76 for GWG below, and 0.65 for GWG above. The LightGBM also performed well with an AUC-ROC of 0.79 for predicting GWG within recommendations, 0.76 for GWG below, and 0.624 for GWG above. The most important predictors of GWG were pre-gestational BMI, maternal age, glycemic profile, hemoglobin levels, and arm circumference. Conclusion: Machine learning models can effectively predict GWG categories, offering a valuable tool for early identification of at-risk pregnancies. This approach can enhance personalized prenatal care and interventions to promote optimal pregnancy outcomes. [ABSTRACT FROM AUTHOR]

Background and Objectives: Cardiovascular disease risk can exist in utero, influenced by maternal health factors. This study evaluates maternal characteristics and biochemical markers that correlate with the fetal intima–media thickness (IMT), aiming to identify interventions that could minimize prenatal influences on later cardiovascular disease. Methods: In this observational study approved by the Institutional Review Board at The Obstetrics and Gynecology Clinic of the Timisoara Municipal Emergency Hospital, we recruited pregnant women aged 15–40 years, divided into groups based on their lipid profiles and gestational diabetes risk. The data collection had, as its main focus, ultrasound measurements, along with demographic, clinical, and biochemical parameters. The IMT of the fetal abdominal aorta was measured at 28 weeks of gestation. Results: Notable differences were observed in the TNF-alpha levels (8.66 ± 3.87 pg/mL vs. 4.96 ± 3.37 pg/mL), hsCRP levels (0.94 ± 0.46 mg/L vs. 0.60 ± 0.52 mg/L), and the area under the curve (AUC) for hsCRP at 0.738 with a sensitivity of 84.41% and specificity of 79.01%. Compound score 2, integrating inflammatory markers and lipid profiles, exhibited a good diagnostic accuracy (AUC = 0.789) with a sensitivity of 86.35% and specificity of 81.42%. A regression analysis indicated strong associations of TNF-alpha and hsCRP with an increased fetal IMT, suggesting potential early markers of cardiovascular risk, presenting hazard ratios (HRs) of 2.21 (95% CI: 1.15–5.28) and 2.87 (95% CI: 1.11–4.23), respectively, both with p-values of less than 0.0001. Compound score 2 further indicated an increased risk (HR = 4.27; 95% CI: 1.19–8.32). Conclusions: Statistically significant correlations were found between an increased fetal IMT and elevated maternal inflammatory markers (TNF-alpha and hsCRP), suggesting that these could serve as early indicators of cardiovascular risk. This study supports the potential for targeted prenatal interventions to reduce cardiovascular risk factors from the fetal stage, emphasizing the importance of monitoring inflammatory markers in pregnant women at risk. [ABSTRACT FROM AUTHOR]

Hypertensive disorders of pregnancy (HDP), such as preeclampsia and eclampsia, present significant risks to maternal and fetal health. While immediate complications are well-documented, emerging research highlights potential neurocognitive impacts on both mothers and their offspring. This narrative review synthesizes evidence on these neurocognitive outcomes associated with HDP, focusing on preeclampsia and eclampsia. A literature search was conducted for studies published from 2000 to February 2024. Maternal outcomes, including memory, executive function, and psychosocial well-being, were assessed across 11 studies, while fetal and neonatal neurocognitive outcomes were explored in five studies. Consistent findings indicate that preeclampsia and eclampsia are linked to impairments in maternal cognitive functions and psychosocial health. Offspring exposed to these conditions in utero also show cognitive deficits and alterations in brain connectivity. Contributing factors include placental dysfunction, altered angiokine levels, maternal stress, and socioeconomic variables. To mitigate these impacts, future research should focus on clarifying the underlying mechanisms and developing early interventions. This review emphasizes the necessity of multidisciplinary approaches to improve neurocognitive outcomes for both mothers and their children affected by preeclampsia and eclampsia. [ABSTRACT FROM AUTHOR]

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Abstract Background Gestational weight gain (GWG) is a critical factor influencing maternal and fetal health. Excessive or insufficient GWG can lead to various complications, including gestational diabetes, hypertension, cesarean delivery, low birth weight, and preterm birth. This study aims to develop and evaluate machine learning models to predict GWG categories: below, within, or above recommended guidelines. Methods We analyzed data from the Araraquara Cohort, Brazil, which comprised 1557 pregnant women with a gestational age of 19 weeks or less. Predictors included socioeconomic, demographic, lifestyle, morbidity, and anthropometric factors. Five machine learning algorithms (Random Forest, LightGBM, AdaBoost, CatBoost, and XGBoost) were employed for model development. The models were trained and evaluated using a multiclass classification approach. Model performance was assessed using metrics such as area under the ROC curve (AUC-ROC), F1 score and Matthew’s correlation coefficient (MCC). Results The outcomes were categorized as follows: GWG within recommendations (28.7%), GWG below (32.5%), and GWG above recommendations (38.7%). The XGBoost presented the best overall model, achieving an AUC-ROC of 0.79 for GWG within, 0.76 for GWG below, and 0.65 for GWG above. The LightGBM also performed well with an AUC-ROC of 0.79 for predicting GWG within recommendations, 0.76 for GWG below, and 0.624 for GWG above. The most important predictors of GWG were pre-gestational BMI, maternal age, glycemic profile, hemoglobin levels, and arm circumference. Conclusion Machine learning models can effectively predict GWG categories, offering a valuable tool for early identification of at-risk pregnancies. This approach can enhance personalized prenatal care and interventions to promote optimal pregnancy outcomes.

Abstract Hypertensive disorders of pregnancy (HDP), such as preeclampsia and eclampsia, present significant risks to maternal and fetal health. While immediate complications are well-documented, emerging research highlights potential neurocognitive impacts on both mothers and their offspring. This narrative review synthesizes evidence on these neurocognitive outcomes associated with HDP, focusing on preeclampsia and eclampsia. A literature search was conducted for studies published from 2000 to February 2024. Maternal outcomes, including memory, executive function, and psychosocial well-being, were assessed across 11 studies, while fetal and neonatal neurocognitive outcomes were explored in five studies. Consistent findings indicate that preeclampsia and eclampsia are linked to impairments in maternal cognitive functions and psychosocial health. Offspring exposed to these conditions in utero also show cognitive deficits and alterations in brain connectivity. Contributing factors include placental dysfunction, altered angiokine levels, maternal stress, and socioeconomic variables. To mitigate these impacts, future research should focus on clarifying the underlying mechanisms and developing early interventions. This review emphasizes the necessity of multidisciplinary approaches to improve neurocognitive outcomes for both mothers and their children affected by preeclampsia and eclampsia.

Background: Modern management of preeclampsia can be optimized by tailoring the targeted treatment of hypertension to an individual's hemodynamic profile. Growing evidence suggests different phenotypes of preeclampsia, including those with a hyperdynamic profile and those complicated by uteroplacental insufficiency. Fetal growth restriction (FGR) is believed to be a result of uteroplacental insufficiency. There is a paucity of research examining the characteristics of patients with severe preeclampsia who do and who do not develop FGR. We aimed to elucidate which hemodynamic parameters differed between these two groups. Methods: All patients admitted to a single referral center with severe preeclampsia were identified. Patients were included if they had a live birth at 23 weeks of gestation or higher. Multiple gestations and pregnancies complicated by fetal congenital anomalies and/or HELLP syndrome were excluded. FGR was defined as a sonographic estimation of fetal weight (EFW) < 10th percentile or abdominal circumference (AC) < 10th percentile. Results: There were 76% significantly lower odds of overall pulse pressure upon admission for those with severe preeclampsia comorbid with FGR (aOR = 0.24, 95% CI = 0.07–0.83). Advanced gestational age on admission was associated with lower odds of severely abnormal labs and severely elevated diastolic blood pressure in preeclampsia also complicated by FGR. Conclusions: Subtypes of preeclampsia with and without FGR may be hemodynamically evaluated by assessing pulse pressure on admission. [ABSTRACT FROM AUTHOR]

Fetal health care is vital in ensuring the health of pregnant women and the fetus. Regular check-ups need to be taken by the mother to determine the status of the fetus' growth and identify any potential problems. To know the status of the fetus, doctors monitor blood reports, Ultrasounds, cardiotocography (CTG) data, etc. Still, in this research, we have considered CTG data, which provides information on heart rate and uterine contractions during pregnancy. Several researchers have proposed various methods for classifying the status of fetus growth. Manual processing of CTG data is time-consuming and unreliable. So, automated tools should be used to classify fetal health. This study proposes a novel neural network-based architecture, the Dynamic Multi-Layer Perceptron model, evaluated from a single layer to several layers to classify fetal health. Various strategies were applied, including pre-processing data using techniques like Balancing, Scaling, Normalization hyperparameter tuning, batch normalization, early stopping, etc. to enhance the model's performance. A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy (97%). An ablation study without any pre-processing techniques is also illustrated. This study easily provides valuable interpretations for healthcare professionals in the decision-making process. [ABSTRACT FROM AUTHOR]

Background and Objectives: Hemolytic disease of the fetus and newborn (HDFN) occurs as a result of maternal sensitization to paternal antigens. Studies have shown that the immunoglobulin G (IgG) subclass in maternal serum – particularly the presence of IgG1 and IgG3 either singly or in combination, has an impact on the presence and severity of HDFN. The column agglutination technology is used for the identification of IgG1 and IgG3 subclasses. Therefore, the prevalence of IgG1 and IgG3 subclasses in a cohort of alloimmunized Rh (D) antenatal women at our center was studied and was correlated with severity of HDFN. The aims of this study were as follows: (1) to find out the prevalence of IgG1 and IgG3 subclasses among Rh (D) alloimmunized women and (2) to assess the clinical outcome of newborns born to mothers with respect to IgG subclass. Methodology: This was a descriptive study done on 75 antenatal Rh (D) alloimmunized women who fulfilled the inclusion criteria. Blood samples from study participants were subjected to IgG subtyping using the “DAT IgG1/IgG3 ID card” (Bio-Rad, Switzerland), and the outcome of pregnancy was assessed by categorizing the newborns into mild, moderate, and severe HDFN. The data obtained were analyzed using SPSS version 16. Prevalence was calculated and severity of HDFN was correlated with subclass. Results: Among the study population, 23% of mothers had IgG1 alone. Majority (72%) had both IgG1 + IgG3 subclasses and a few (5%) had IgG2 + IgG4. When the mother had both IgG1 + IgG3 subclasses in their body most newborns had only mild HDFN. Few cases of moderate-to-severe cases of HDFN were seen. Most newborns had only mild HDFN even though there were few moderate-to-severe cases of HDFN. No significant association could be demonstrated between severity of HDFN and type of IgG subclasses. Conclusion: Majority of maternal IgG found in HDFN was a combination of IgG1+IgG3.Such cases exhibited stronger grades of reaction in Indirect Coombs Test. A significant association could not be demonstrated between severity of HDFN and type of IgG subclasses.

Objective (s): Prenatal care is essential for the birth of healthy babies and the maintenance of mothers' health. This study was conducted to explore the experiences of mothers regarding prenatal care at the Sureshjan Health Center. Methods: This was a qualitative study using conventional analysis of the data. The study population consisted of 20 pregnant women from Sureshjan, Chaharmahal, and Bakhtiari province. A semi-structure in-depth interviews conducted to collect the data. Interviews continued until data saturation. The data were analyzed using an inductive method that involved open coding, category creation, and abstraction. Results: The results showed that participants pay serious attention to prenatal care and see it as a vital necessity for that period. The following three categories emerged from the analysis: maintaining and promoting maternal health, supporting fetal health, and maternal care literacy. Conclusion: The pregnant mothers were aware of the importance of prenatal care and considered it necessary to maintain their health and the fetus, as well as to learn important skills related to pregnancy and infancy.

Objectives Fetal health locus of control has been indicated to effect how mothers approach their health beliefs and health-related decisions. The purpose of this study was to determine the fetal health locus of control (FHLC) level in high-risk pregnancies and to evaluate the factors affecting FHLC in high-risk pregnancies. Materials and Methods The descriptive cross-sectional study was conducted with highrisk pregnant women. A total of 221 participants were included. Results and Discussion It was shown that The Internality Locus of Control was low and The Chance-Based Locus of Control was high in high-risk pregnant women. The current age of women and the age of their first pregnancy had a positively relationship with Powerful Others Locus of Control (FHLC-P). Although FHLC scores of multigravidas were higher than primiparous, no significant difference was found. Besides, there was a significantly higher FHLC-C score in women who did not change their exercise habits during pregnancy (p=0.008). Although the FHLC-I score was higher in women, who made changes in nutrition habits, it was determined that it was not significant. Conclusion The results of fetal health locus of control in high-risk pregnancies will lead to women who have high-risk pregnancies taking responsibility for adapting to the treatment process, taking into account the controls and making lifestyle changes, rather than leaving the situation to fate. [ABSTRACT FROM AUTHOR]

• An AI method was trained to predict fetal heartrate. • This method is expected to fail for abnormal CTG. • The prediction error correlates to FIGO interpretation. • This method might therefore be used in clinical decision support. To assess whether artificial intelligence, inspired by clinical decision-making procedures in delivery rooms, can correctly interpret cardiotocographic tracings and distinguish between normal and pathological events. Study Design : A method based on artificial intelligence was developed to determine whether a cardiotocogram shows a normal response of the fetal heart rate to uterine activity (UA). For a given fetus and given the UA and previous FHR, the method predicts a fetal heart rate response, under the assumption that the fetus is still in good condition and based on how that specific fetus has responded so far. We hypothesize that this method, when having only learned from fetuses born in good condition, is incapable of predicting the response of a compromised fetus or an episode of transient fetal distress. The (in)capability of the method to predict the fetal heart rate response would then yield a method that can help to assess fetal condition when the obstetrician is in doubt. Cardiotocographic data of 678 deliveries during labor were selected based on a healthy outcome just after birth. The method was trained on the cardiotocographic data of 548 fetuses of this group to learn their heart rate response. Subsequently it was evaluated on 87 fetuses, by assessing whether the method was able to predict their heart rate responses. The remaining 43 cardiotocograms were segment-by-segment annotated by three experienced gynecologists, indicating normal, suspicious, and pathological segments, while having access to the full recording and neonatal outcome. This future knowledge makes the expert annotations of a quality that is unachievable during live interpretation. The comparison between abnormalities detected by the method (only using past and present input) and the annotated CTG segments by gynecologists (also looking at future input) yields an area under the curve of 0.96 for the distinction between normal and pathological events in majority-voted annotations. The developed method can distinguish between normal and pathological events in near real-time, with a performance close to the agreement between three gynecologists with access to the entire CTG tracing and fetal outcome. The method has a strong potential to support clinicians in assessing fetal condition in clinical practice. [ABSTRACT FROM AUTHOR]

Objective (s): Prenatal care is essential for the birth of healthy babies and the maintenance of mothers' health. This study was conducted to explore the experiences of mothers regarding prenatal care at the Sureshjan Health Center. Methods: This was a qualitative study using conventional analysis of the data. The study population consisted of 20 pregnant women from Sureshjan, Chaharmahal, and Bakhtiari province. A semi-structure in-depth interviews conducted to collect the data. Interviews continued until data saturation. The data were analyzed using an inductive method that involved open coding, category creation, and abstraction. Results: The results showed that participants pay serious attention to prenatal care and see it as a vital necessity for that period. The following three categories emerged from the analysis: maintaining and promoting maternal health, supporting fetal health, and maternal care literacy. Conclusion: The pregnant mothers were aware of the importance of prenatal care and considered it necessary to maintain their health and the fetus, as well as to learn important skills related to pregnancy and infancy. [ABSTRACT FROM AUTHOR]

Objective: In the present study, it was aimed to adapt the fetal health anxiety inventory (FHAI) into Turkish and to analyze the validity and reliability of the scale among pregnant women. Methods: Explanatory factor analysis (EFA) was applied to 370 pregnant women in Sample I and confirmatory factor analysis (CFA) was applied to 200 pregnant women in Sample II. The Prenatal Distress Questionnaire (NuPDQ) was used to test criterion-related validity of the FHAI. The reliability of the inventory was examined with Cronbach’s alpha reliability coefficient, item-total score correlation coefficient and test-retest analysis. Results: As a result of EFA applied to Sample I, it was determined that the 14-item FHAI covered a single factor, and the scale demonstrated good fit indices (χ2/standard deviation =3.148, comparative fit index =0.907, standardized root mean squared residual =0.000, root mean square error of approximation =0.089, and p value =0.000) as a result of the CFA applied to Sample II. A statistically significant positive correlation was found between the FHAI and NuPDQ (r=0.851, p

Obstetricians utilize cardiotocography (CTG) to assess the fetal heart and lungs during pregnancy. It can help determine if the foetus is healthy, in doubt, or suffering from disease by providing data on the fetal heart rate and uterine breathing. The analysis of CTG data has typically made use of machine learning (ML) techniques such as support vector machines and decision trees to forecast fetal health and enhance the detection procedure. Fetal heart rate and uterine contraction timing were recorded by CTG. Monitoring fetal health and ensuring normal fetal growth and development throughout pregnancy rely heavily on CTG intelligent categorization. Pregnancies with a higher risk of problems are the most common cases in which CTG is used to evaluate the health of the fetus. ML algorithms are utilized to evaluate state of the foetal health based on CTG-obtained factors. Compared to ML techniques, ensemble models have been shown to increase detection speed and effectiveness. Ensemble modeling refers to the practice of combining the scores or distributions from multiple related but distinct analytical models. In order to predict foetal health, ensemble models such as Boosting, AdaBoost, Extreme Gradient Boosting, Light gradient boost method (LightGBM), and stack models were used in the paper. When the outcomes are compared, the proposed stack model using logistic regression, decision tree, random forest and LightGBM proved to obtain the best performance with 96.71% success rate. The proposed methodology, which can be used to classify foetal health based on Fetal Heart Rate (FHR) data, is more efficient and superior to existing machine learning models, which have already been taken into consideration. [ABSTRACT FROM AUTHOR]

Objective: In the present study, it was aimed to adapt the fetal health anxiety inventory (FHAI) into Turkish and to analyze the validity and reliability of the scale among pregnant women. Methods: Explanatory factor analysis (EFA) was applied to 370 pregnant women in Sample I and confirmatory factor analysis (CFA) was applied to 200 pregnant women in Sample II. The Prenatal Distress Questionnaire (NuPDQ) was used to test criterion-related validity of the FHAI. The reliability of the inventory was examined with Cronbach’s alpha reliability coefficient, item-total score correlation coefficient and test-retest analysis. Results: As a result of EFA applied to Sample I, it was determined that the 14-item FHAI covered a single factor, and the scale demonstrated good fit indices (χ²/standard deviation =3.148, comparative fit index =0.907, standardized root mean squared residual =0.000, root mean square error of approximation =0.089, and p value =0.000) as a result of the CFA applied to Sample II. A statistically significant positive correlation was found between the FHAI and NuPDQ (r=0.851, p<0.01). Cronbach’s alpha internal consistency coefficient of the inventory was 0.85, and item-total score correlation coefficients were found to range between r=0.34- 0.59 (p<0.001). In the test-retest analysis, a statistically significant and positive correlation was found between the total scores of the inventory obtained in two different applications (r=0.568, p=0.001). Conclusion: The current study supported the use of 14-item FHAI as a valid and reliable tool to measure fetal health anxiety of Turkish pregnant women. [ABSTRACT FROM AUTHOR]

Background and Objectives: Hemolytic disease of the fetus and newborn (HDFN) occurs as a result of maternal sensitization to paternal antigens. Studies have shown that the immunoglobulin G (IgG) subclass in maternal serum - particularly the presence of IgG1 and IgG3 either singly or in combination, has an impact on the presence and severity of HDFN. The column agglutination technology is used for the identification of IgG1 and IgG3 subclasses. Therefore, the prevalence of IgG1 and IgG3 subclasses in a cohort of alloimmunized Rh (D) antenatal women at our center was studied and was correlated with severity of HDFN. The aims of this study were as follows: (1) to find out the prevalence of IgG1 and IgG3 subclasses among Rh (D) alloimmunized women and (2) to assess the clinical outcome of newborns born to mothers with respect to IgG subclass. Methodology: This was a descriptive study done on 75 antenatal Rh (D) alloimmunized women who fulfilled the inclusion criteria. Blood samples from study participants were subjected to IgG subtyping using the "DAT IgG1/IgG3 ID card" (Bio-Rad, Switzerland), and the outcome of pregnancy was assessed by categorizing the newborns into mild, moderate, and severe HDFN. The data obtained were analyzed using SPSS version 16. Prevalence was calculated and severity of HDFN was correlated with subclass. Results: Among the study population, 23% of mothers had IgG1 alone. Majority (72%) had both IgG1 + IgG3 subclasses and a few (5%) had IgG2 + IgG4. When the mother had both IgG1 + IgG3 subclasses in their body most newborns had only mild HDFN. Few cases of moderate-to-severe cases of HDFN were seen. Most newborns had only mild HDFN even though there were few moderate-to-severe cases of HDFN. No significant association could be demonstrated between severity of HDFN and type of IgG subclasses. Conclusion: Majority of maternal IgG found in HDFN was a combination of IgG1+IgG3.Such cases exhibited stronger grades of reaction in Indirect Coombs Test. A significant association could not be demonstrated between severity of HDFN and type of IgG subclasses. [ABSTRACT FROM AUTHOR]

Government policies often target vulnerable communities on the premise of their socioeconomic status, race, gender, etc., and hinder their ability to attain proper resources. These discriminatory practices can have consequences that span generations when enforced upon expecting mothers that lack access to preventative care. Whether it be for education, basic needs, or even health, access is undoubtedly disproportionate. Redlining is a specific policy that has seeped its way into communities and continues to affect the socioeconomic status of hundreds of thousands of families. One aspect of health that is especially impacted is maternal and fetal health. Findings show increased health risks for mothers and an increase in potential fetal mortality in these areas. Expanding research on the topic is crucial to promoting policies and the expansion of programs that will help subside these harmful effects that pose a risk to these communities. Our environments, both physical and social, are determinants of our health and in reviewing existing literature, it is clear that where we live is vital to our livelihoods.

Abstract Background Bipolar disorder (BD) is a mental disorder characterized by mood shifts from severe depression to mania. Pregnant women with BD may experience manic or depressive episodes, so they are usually concerned about the effects of BD on their pregnancy. The aim of this systematic review is to determine the effects of BD on maternal health and fetal health, weight, and development. It also addresses how BD affects the probability of incidence of pregnancy complications in women with bipolar compared with healthy controls. Methods Seven electronic databases (Ovid MEDLINE, Embase, MIDRIS, APA PsychINFO, Scopus, Web of Science, and ScienceOpen) were searched, and 1728 eligible studies were identified. After deduplication, screening, and manual search processes, we included only 15 studies. Descriptive analysis, and calculation of the probability of incidence for each pregnancy outcome were used to analyze the results. Results The findings of the included studies suggest that BD during pregnancy may affect both fetal growth and maternal health by increasing the risk of giving birth to an infant with some birth defects such as microcephaly, CNS problems, small for gestational age, and other congenital anomalies, in addition to causing some obstetric complications such as gestational hypertension, preterm labor, need for assisted delivery, hospital readmission, and others. Conclusion Bipolar disorder during pregnancy negatively affects mothers and their fetuses and increases the probability of incidence of obstetrics complications.

Cardiotocography (CTG) represents the fetus's health inside the womb during labor. However, assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician. Digital signals from fetal monitors acquire parameters (i.e., fetal heart rate, contractions, acceleration). Objective: This paper aims to classify the CTG readings containing imbalanced healthy, suspected, and pathological fetus readings. Method: We perform two sets of experiments. Firstly, we employ five classifiers: Random Forest (RF), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM) without over-sampling to classify CTG readings into three categories: healthy, suspected, and pathological. Secondly, we employ an ensemble of the above-described classifiers with the oversamplingmethod. We use a random over-sampling technique to balance CTG records to train the ensemble models. We use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate them. The outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate results. Results: Each classifier evaluates accuracy, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values. Results reveal that the XGBoost, LGBM, and CatBoost classifiers yielded 99% accuracy. Conclusion: Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment. A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model. [ABSTRACT FROM AUTHOR]

Amniotic fluid is essential for fetus wellbeing and is used to monitor pregnancy and predict fetal outcomes. Sex affects health and medicine from the beginning of life, but knowledge of its influence on cell-depleted amniotic fluid (AF) and amniotic fluid cells (AFCs) is still neglected. We evaluated sex-related differences in AF and in AFCs to extend personalized medicine to prenatal life. AFCs and AF were obtained from healthy Caucasian pregnant women who underwent amniocentesis at the 16th–18th week of gestation for advanced maternal age. In the AF, inflammation biomarkers (TNFα, IL6, IL8, and IL4), malondialdehyde, nitrites, amino acids, and acylcarnitines were measured. Estrogen receptors and cell fate (autophagy, apoptosis, senescence) were measured in AFCs. TNFα, IL8, and IL4 were higher in female AF, whereas IL6, nitrites, and MDA were similar. Valine was higher in male AF, whereas several acylcarnitines were sexually different, suggesting a mitochondrial involvement in establishing sex differences. Female AFCs displayed higher expression of ERα protein and a higher ERα/ERβ ratio. The ratio of LC3II/I, an index of autophagy, was higher in female AFCs, while LC3 gene was similar in both sexes. No significant sex differences were found in the expression of the lysosomal protein LAMP1, while p62 was higher in male AFCs. LAMP1 gene was upregulated in male AFCs, while p62 gene was upregulated in female ones. Finally, caspase 9 activity and senescence linked to telomeres were higher in female AFCs, while caspase 3 and β-galactosidase activities were similar. This study supports the idea that sex differences start very early in prenatal life and influence specific parameters, suggesting that it may be relevant to appreciate sex differences to cover knowledge gaps. This might lead to improving the diagnosis of risk prediction for pregnancy complications and achieving a more satisfactory monitoring of fetus health, even preventing future diseases in adulthood. [ABSTRACT FROM AUTHOR]

Background: Bipolar disorder (BD) is a mental disorder characterized by mood shifts from severe depression to mania. Pregnant women with BD may experience manic or depressive episodes, so they are usually concerned about the effects of BD on their pregnancy. The aim of this systematic review is to determine the effects of BD on maternal health and fetal health, weight, and development. It also addresses how BD affects the probability of incidence of pregnancy complications in women with bipolar compared with healthy controls. Methods: Seven electronic databases (Ovid MEDLINE, Embase, MIDRIS, APA PsychINFO, Scopus, Web of Science, and ScienceOpen) were searched, and 1728 eligible studies were identified. After deduplication, screening, and manual search processes, we included only 15 studies. Descriptive analysis, and calculation of the probability of incidence for each pregnancy outcome were used to analyze the results. Results: The findings of the included studies suggest that BD during pregnancy may affect both fetal growth and maternal health by increasing the risk of giving birth to an infant with some birth defects such as microcephaly, CNS problems, small for gestational age, and other congenital anomalies, in addition to causing some obstetric complications such as gestational hypertension, preterm labor, need for assisted delivery, hospital readmission, and others. Conclusion: Bipolar disorder during pregnancy negatively affects mothers and their fetuses and increases the probability of incidence of obstetrics complications. [ABSTRACT FROM AUTHOR]

(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer's output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate how women of child-bearing age perceive the use of remote fetal ECG monitoring technologies. Telemedicine has advanced to the forefront of healthcare delivery, including maternal–fetal medicine. Smart wearable electrocardiogram (ECG) devices can enable pregnant women to monitor their health and that of their fetuses. Such technology would be a logical extension of the telemedicine ecosystem. Methods: We conducted an observational cross-sectional study via online surveying in the United States. Study participants were recruited using the SurveyMonkey Audience Polling system and responded virtually. In all, the sample consisted of 507 women, aged 18–45 from 45 states, who are expecting to become pregnant in the next five years. Women were asked to identify their willingness to use a wearable ECG device the size of a patch-sized large band-aid on their abdomen. Ten binary or multiple-choice questions were used to gauge population interest and related demographics toward the usage of a wearable ECG device. Results: Of the 507 participants, 461 (91%) women expressed an acceptance of wearable ECG technology throughout the pregnancy as a mechanism for increased frequency of monitoring of maternal and fetal health outside the hospital. 395 (78%) women demonstrated a willingness to wear devices day and night or at least during sleep and 213 (42%) of the women would spend up to $200 on such a device. Conclusion: Even though conducted prior to the COVID-19 pandemic, this study clearly indicates a high degree of readiness of prospective pregnant women for telemedicine with continuous health monitoring of the mother-fetus dyad. [ABSTRACT FROM AUTHOR]

Excessive accumulation of advanced glycation end products (AGEs) in the body has been associated with many adverse health conditions. The common point of the pathologies associated at this point is oxidative stress and inflammation. Pregnancy is an important period in which many physiological, psychological, and biological changes are experienced. Along with the physiological changes that occur during this period, the mother maintaining an AGE-rich diet may cause an increase in the body's AGE pool and may increase oxidative stress and inflammation, as seen in healthy individuals. Studies have reported the negative effects of maternal AGE levels on maternal and fetal health during pregnancy. Although gestational diabetes, preeclampsia, endothelial dysfunction, and pelvic diseases constitute maternal complications, a number of pathological conditions such as intrauterine growth retardation, premature birth, neural tube defect, neurobehavioral developmental disorders, fetal death, and neonatal asphyxia constitute fetal complications. It is thought that the mechanisms of these complications have not been confirmed yet and more clinical studies are needed on this subject. The possible effects of dietary AGE levels during pregnancy on maternal and fetal health are examined in this review. [ABSTRACT FROM AUTHOR]

Persistent organic pollutants (POPs) are toxic chemicals with demonstrable effects on pregnancy and neonatal outcomes. The associations of early pregnancy body mass index (BMI) and antenatal weight changes with circulating POP concentrations are poorly understood in the Canadian context. The aim of this study was to explore the relationship between maternal BMI in the first trimester, weight change from pre-pregnancy to 6–13 weeks of pregnancy, and first-trimester plasma POP concentrations among Canadian pregnant women. We analyzed data collected as part of the Maternal-Infant Research on Environmental Chemicals (MIREC) study and evaluated POP concentrations based on first-trimester BMI and early gestational weight change categories. We tested for overall differences using Kruskal-Wallis tests. The associations between first-trimester maternal BMI, weight change, and plasma concentrations of 41 POPs were evaluated using censored regression models. After controlling for potential confounders, first-trimester plasma levels of multiple POPs differed significantly across BMI categories, with the highest concentrations in underweight/normal-weight individuals and the lowest in class III obese individuals. Our findings provide preliminary evidence of higher circulating POP levels in individuals with obesity and align with previous findings of an inverse relationship between circulating POP concentrations and BMI in pregnancy. Future studies should prospectively evaluate the interplay between weight change and POP concentrations throughout pregnancy to inform gestational weight gain recommendations for pregnant individuals with obesity. [ABSTRACT FROM AUTHOR]

In today’s medical field, doctors rely on electrocardiograms (ECG) to monitor fetal heart rate and overall fetal health during labor. ECG readings, although accurate, tend to be very difficult to interpret and consequently tell doctors very little in terms of fetal distress levels during labor. Due to this lack of specificity, physicians often turn to emergency intervention by performing Cesarean sections (C-sections). Nearly half of these C-sections are unnecessary which poses extreme risks to the fetus and the mother, and is a financial burden on insurance companies as well. To combat these issues, our team proposes pHetal Meter, a handheld device that can give a rapid, bedside reading of fetal blood pH. Fetal blood pH levels are currently the gold standard for early detection of fetal distress during labor. The device will be able to quickly and accurately detect whether or not the fetus is in distress. Our device will be nurse-operable, cause little to no pain or discomfort to the mother, and allow for faster and more direct results. When used alongside current technology, physicians will no longer need to guess whether an emergency C-section is necessary, and will be able to rapidly determine fetal distress levels. pHetal Meter uniquely bridges an unmet medical need by quickly and accurately determining intrapartum fetal health, and drastically lowering the risks and costs incurred by unnecessary C-sections. Advisors: Christine King, Ph.D. Department of Biomedical EngineeringDinh Vu, MD, MBA, Department of Obstetrics and Gynecology, Saddleback Medical CenterDepartment of Biomedical engineering and material Science Engineering, University of California Irvine

Omega (n)-3 fatty acids are vital to neonatal maturation, and recent investigations reveal n-3 fatty acids serve as substrates for the biosynthesis of specialized pro-resolving lipid mediators (SPM) that have anti-inflammatory and immune-stimulating effects. The role SPM play in the protection against negative maternal-fetal health outcomes is unclear, and there are no current biomarkers of n-3 fatty acid sufficiency. We sought to ascertain the relationships between n-3 fatty acid intake, SPM levels, and maternal-fetal health outcomes. We obtained n-3 fatty acid intake information from 136 mothers admitted for delivery using a food frequency questionnaire and measured docosahexaenoic acid (DHA)-derived SPMs resolvin D1 (RvD1) and RvD2 in maternal and cord plasma. We found significantly elevated SPM in maternal versus cord plasma, and increased SPM levels were associated with at-risk outcomes. We also identified that increased DHA intake was associated with elevated maternal plasma RvD1 (p = 0.03; R² = 0.18) and RvD2 (p = 0.04; R² = 0.20) in the setting of neonatal intensive care unit (NICU) admission. These findings indicate that increased n-3 fatty acid intake may provide increased substrate for the production of SPM during high-risk pregnancy/delivery conditions, and that increased maternal plasma SPM could serve as a biomarker for negative neonatal outcomes.

Metallic elements play a pivotal role in maternal and fetal health. Metals can cross the placental barrier and be absorbed by fetuses, where they may affect closure of the neural tube during embryonic development. Neural tube defects (NTDs), which result from aberrant closure of the neural tube three to four weeks post-conception, have a multifactorial and complex etiology that combines genetic variants and environmental exposure. Recent advances in population-level association studies have investigated the link between maternal environmental exposure and NTDs, particularly the influence of metals on the incidence of NTDs. Herein, we present a broad and qualitative review of current literature on the association between maternal and prenatal metal exposure via the maternal peripheral blood, amniotic fluid, placenta, umbilical cord, and maternal hair, and the risk of developing NTDs. Specifically, we identify the various aggravating or attenuating effects of metallic exposure on the risk of NTD formation. This review provides novel insights into the association between environmental metals and NTDs and has important applications for NTD prevention and mitigating environmental exposure to metals.

The increase in substance use, whether legal or illegal, represents a growing global public health problem, affecting various social groups, including pregnant women. The main objective of this research is to describe the use of drugs during pregnancy and to analyze the harmful effects that these substances can cause both in the mother and in the fetus. To carry out this study, a qualitative research methodology of exploratory documentary type was used. Relevant data was collected and selected by reviewing documents, books, and journals published from 2015 to the present. This exhaustive bibliographical review provided us with the necessary support to develop this research work. The findings showed that the consumption of illicit drugs during pregnancy entails a high-risk situation both for the health of the mother and for the development of the fetus. In addition, it was possible to establish that the onset and duration of the neonatal abstinence syndrome vary depending on the type of drug consumed, the amount ingested, and the time of consumption in relation to the date of delivery. In the conclusions of the study, it has been shown that the use of drugs, both legal and illegal, is more frequent in pregnant women. There are many complications that can arise because of drug use during pregnancy, affecting both the mother and the fetus. [ABSTRACT FROM AUTHOR]

Normal pregnancy relies on inflammation for implantation, placentation, and parturition, but uncontrolled inflammation can lead to poor maternal and infant outcomes. Maternal diet is one modifiable factor that can impact inflammation. Omega-3 and -6 fatty acids obtained through the diet are metabolized into bioactive compounds that effect inflammation. Recent evidence has shown that the downstream products of omega-3 and -6 fatty acids may influence physiology during pregnancy. In this review, the current knowledge relating to omega-3 and omega-6 metabolites during pregnancy will be summarized. [ABSTRACT FROM AUTHOR]

Clinical trials to address the COVID-19 public health emergency have broadly excluded pregnant people from participation, illustrating a long-standing trend of clinical trial exclusion that has led to a clear knowledge gap and unmet need in the treatment and prevention of medical conditions experienced during pregnancy and of pregnancy-related conditions. Drugs (includes products such as drugs, biologics, biosimilars and vaccines) approved for a certain medical condition in adults are also approved for use in pregnant adults with the same medical condition, unless contraindicated for use in pregnancy. However, there are limited pregnancy-specific data on risks and benefits of drugs in pregnant people, despite their approval for all adults. The United States Food and Drug Administration-approved medical products are used widely by pregnant people, 90% of whom take at least 1 medication during the course of their pregnancy despite there being sparse data from clinical trials on these products in pregnancy. This overall lack of clinical data precludes informed decision-making, causing clinicians and pregnant patients to have to decide whether to pursue treatment without an adequate understanding of potential effects. Although some United States Food and Drug Administration initiatives and other federal efforts have helped to promote the inclusion of pregnant people in clinical research, broader collaboration and reforms are needed to address challenges related to the design and conduct of trials that enroll pregnant people, and to forge a culture of widespread inclusion of pregnant people in clinical research. This article summarizes the scientific, ethical, and legal considerations governing research conducted during pregnancy, as discussed during a recent subject matter expert convening held by the Duke-Margolis Center for Health Policy and the United States Food and Drug Administration on this topic. This article also recommends strategies for overcoming impediments to inclusion and trial conduct. [ABSTRACT FROM AUTHOR]

(1) Background: According to the World Health Organization (WHO), 6.3 million intrauterine fetal deaths occur every year. The most common method of diagnosing perinatal death and taking early precautions for maternal and fetal health is a nonstress test (NST). Data on the fetal heart rate and uterus contractions from an NST device are interpreted based on a trace printer’s output, allowing for a diagnosis of fetal health to be made by an expert. (2) Methods: in this study, a predictive method based on ensemble learning is proposed for the classification of fetal health (normal, suspicious, pathology) using a cardiotocography dataset of fetal movements and fetal heart rate acceleration from NST tests. (3) Results: the proposed predictor achieved an accuracy level above 99.5% on the test dataset. (4) Conclusions: from the experimental results, it was observed that a fetal health diagnosis can be made during NST using machine learning.

The productive replication of human influenza viruses is almost exclusively restricted to cells in the respiratory tract. However, a key aspect of the host response to viral infection is the production of inflammatory cytokines and chemokines that are not similarly tissue restricted. As such, circulating inflammatory mediators, as well as the resulting activated immune cells, can induce damage throughout the body, particularly in individuals with underlying conditions. As a result, more holistic experimental approaches are required to fully understand the pathogenesis and scope of influenza virus‐induced disease. This review summarizes what is known about some of the most well‐appreciated nonrespiratory tract sites of influenza virus‐induced disease, including neurological, cardiovascular, gastrointestinal, muscular and fetal developmental phenotypes. In the context of this discussion, we describe the in vivo experimental systems currently being used to study nonrespiratory symptoms. Finally, we highlight important future questions and potential models that can be used for a more complete understanding of influenza virus‐induced disease. [ABSTRACT FROM AUTHOR]

Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi‐layer perceptron, and K‐nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1‐score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data. [ABSTRACT FROM AUTHOR]

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Cardiotocogram (CTG) is one of the monitoring tools to estimate the fetus health in womb. CTG mainly yields two results fetal health rate (FHR) and uterine contractions (UC). In total, there are 21 attributes in the measurement of FHR and UC on CTG. These attributes can help obstreticians to clasify whether the fetus health is normal, suspected, or pathological. This paper compares 7 algorithms to predict the fetus health: Artificial Neural Network (ANN), Long-short Term Memory (LSTM), XG Boost (XGB), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Light GBM (LGBM), and Random Forest (RF). By employing three scenarios, this paper reports the performance measurements among those algorithms. The results show that 5 out of 7 algorithms perform very well (89-99% accurate). Those five algorithms are XGB, SVM, KNN, LGBM, RF. Furthermore, only one from five algorithm that always performs well through three scenarios: LGBM. [ABSTRACT FROM AUTHOR]

Background and Objective: Although pregnancy is a pleasurable period for most women, but generally, it is a stressful period with physical and psychological changes. Mothers’ worries during pregnancy are associated with more problems in pregnancy. Improving well-being is one of the important goals for pregnant women. The aim of this study was to explore the concerns of pregnancy in nulliparous women. Materials and Methods: This qualitative study was performed through the conventional content analysis via using Granhiem and Landman method. Data were collected through semi-structured interviews with 24 primiparous mothers in Kashan and data saturation was obtained. Data analysis was performed using the MAXQDA 10 software. Results: Data analysis led to two categories and subclasses. The first category was the mother’s concerns about her health during pregnancy, which included the following categories: changes in physical and mental conditions during pregnancy and coping with its, healthy childbirth, family and social support, role playing as a spouse, and Parental role challenges. The second category was the mother’s concerns about the health of fetus in pregnancy, which included the following categories: Unpleasant thoughts about fetal formation, responsibility for fetal health, and Fear of fetal injury during pregnancy and childbirth. Conclusion: Pregnant women during pregnancy suffer from a number of concerns about their own health and their fetus, which can threaten the health of both. Therefore, it seems that mothers ‘health care providers can help mothers to experience a safer pregnancy by reducing negative thoughts by holding training classes. [ABSTRACT FROM AUTHOR]

A method for the automatic determination of the fetus health status using Cardiotocography (CTG) and computer-based machine learning algorithms was developed. Five computation friendly machine learning algorithms were used to create multiclass classification models to predict the fetus health status from secondary CTG dataset containing normal, suspected and pathologic data available at University California Irvine Machine Learning Repository. Furthermore, a comparative analysis among the built models was executed. According to the comparative analysis, the best model to automatically detect fetal health was the extreme gradient boosting algorithm-based model with an accuracy of 96.7% and an F1-Score of 0.963 in the pathologic class. This finding thus has the potential to diagnose fetal heart conditions unsupervised, and more efficiently and effectively. [ABSTRACT FROM AUTHOR]