Estimation of umbilical cord blood leptin and insulin based on anthropometric data by means of artificial neural network approach: identifying key maternal and neonatal factors

Development and validation of a prediction model for intrapartum cesarean delivery based on the artificial neural networks approach: a protocol for a prospective nested case-control study

INTRODUCTION

Although intrapartum caesarean delivery can resolve dystocia, it would still lead to several adverse outcomes for mothers and children. The obstetric care professionals need effective tools that can help them to identify the possibility and risk factors of intrapartum caesarean delivery, and further implement interventions to avoid unnecessary caesarean birth. This study aims to develop a prediction model for intrapartum caesarean delivery with real-life data based on the artificial neural networks approach.

METHODS AND ANALYSIS

This study is a prospective nested case-control design. Pregnant women who plan to deliver vaginally will be recruited in a tertiary hospital in Southwest China from March 2022 to March 2024. The clinical data of prelabour, intrapartum period and psychosocial information will be collected. The case group will be the women who finally have a baby with intrapartum caesarean deliveries, and the control group will be those who deliver a baby vaginally. An artificial neural networks approach with the backpropagation algorithm multilayer perceptron topology will be performed to construct the prediction model.

ETHICS AND DISSEMINATION

Ethical approval for data collection was granted by the Ethics Committee of West China Second University Hospital, Sichuan University, and the ethical number is 2021 (204). Written informed consent will be obtained from all participants and they can withdraw from the study at any time. The results of this study will be published in peer-review journal.

Maternal obesity and the impact of associated early-life inflammation on long-term health of offspring

The prevalence of obesity is increasingly common in the United States, with ~25% of women of reproductive age being overweight or obese. Metaflammation, a chronic low grade inflammatory state caused by altered metabolism, is often present in pregnancies complicated by obesity. As a result, the fetuses of mothers who are obese are exposed to an in-utero environment that has altered nutrients and cytokines. Notably, both human and preclinical studies have shown that children born to mothers with obesity have higher risks of developing chronic illnesses affecting various organ systems. In this review, the authors sought to present the role of cytokines and inflammation during healthy pregnancy and determine how maternal obesity changes the inflammatory landscape of the mother, leading to fetal reprogramming. Next, the negative long-term impact on offspring’s health in numerous disease contexts, including offspring’s risk of developing neuropsychiatric disorders (autism, attention deficit and hyperactive disorder), metabolic diseases (obesity, type 2 diabetes), atopy, and malignancies will be discussed along with the potential of altered immune/inflammatory status in offspring as a contributor of these diseases. Finally, the authors will list critical knowledge gaps in the field of developmental programming of health and diseases in the context of offspring of mothers with obesity, particularly the understudied role of hematopoietic stem and progenitor cells.

Contributions of artificial intelligence reported in Obstetrics and Gynecology journals: a systematic review

Background

The applications of artificial intelligence (AI) processes have grown significantly in all medical disciplines during the last decades. Two main types of AI have been applied in medicine: symbolic AI (eg, knowledge base and ontologies) and nonsymbolic AI (eg, machine learning and artificial neural networks). Consequently, AI has also been applied across most obstetrics and gynecology (OB/GYN) domains, including general obstetrics, gynecology surgery, fetal ultrasound, and assisted reproductive medicine, among others.

Objective

The aim of this study was to provide a systematic review to establish the actual contributions of AI reported in OB/GYN discipline journals.

Methods

The PubMed database was searched for citations indexed with “artificial intelligence” and at least one of the following medical subject heading (MeSH) terms between January 1, 2000, and April 30, 2020: “obstetrics”; “gynecology”; “reproductive techniques, assisted”; or “pregnancy.” All publications in OB/GYN core disciplines journals were considered. The selection of journals was based on disciplines defined in Web of Science. The publications were excluded if no AI process was used in the study. Review, editorial, and commentary articles were also excluded. The study analysis comprised (1) classification of publications into OB/GYN domains, (2) description of AI methods, (3) description of AI algorithms, (4) description of data sets, (5) description of AI contributions, and (6) description of the validation of the AI process.

Results

The PubMed search retrieved 579 citations and 66 publications met the selection criteria. All OB/GYN subdomains were covered: obstetrics (41%, 27/66), gynecology (3%, 2/66), assisted reproductive medicine (33%, 22/66), early pregnancy (2%, 1/66), and fetal medicine (21%, 14/66). Both machine learning methods (39/66) and knowledge base methods (25/66) were represented. Machine learning used imaging, numerical, and clinical data sets. Knowledge base methods used mostly omics data sets. The actual contributions of AI were method/algorithm development (53%, 35/66), hypothesis generation (42%, 28/66), or software development (3%, 2/66). Validation was performed on one data set (86%, 57/66) and no external validation was reported. We observed a general rising trend in publications related to AI in OB/GYN over the last two decades. Most of these publications (82%, 54/66) remain out of the scope of the usual OB/GYN journals.

Conclusions

In OB/GYN discipline journals, mostly preliminary work (eg, proof-of-concept algorithm or method) in AI applied to this discipline is reported and clinical validation remains an unmet prerequisite. Improvement driven by new AI research guidelines is expected. However, these guidelines are covering only a part of AI approaches (nonsymbolic) reported in this review; hence, updates need to be considered.

Determinants of placental leptin receptor gene expression and association with measures at birth

INTRODUCTION

The leptin signalling pathway is important in metabolic health during pregnancy. However, few studies have investigated the determinants and extent of leptin receptor gene (LEPR) expression in the placenta, nor the relationship with infant health in early life. Here, we investigate the genetic and maternal in utero determinants of placental LEPR expression, and whether this expression is linked to anthropometric and inflammatory measures at birth in healthy newborns in the Barwon Infant Study.

METHODS

Placental LEPR expression was measured using RT-qPCR (n = 854 placentae). Associations between genetic variation in LEPR, maternal in utero factors, measures at birth and placental LEPR expression were assessed using multivariable linear regression modelling.

RESULTS

We found that the genotype at two intronic SNPs, rs9436301 and rs9436746, was independently associated with placental LEPR expression. Maternal pre-pregnancy body mass index, gestational diabetes mellitus, weight gain and smoking in pregnancy were not associated with LEPR expression. Placental LEPR expression was negatively associated with high sensitivity C-Reactive Protein in umbilical cord blood, which persisted after adjustment for potential confounders.

DISCUSSION

Overall, our findings suggest that genetic variation in LEPR plays a key role in regulating placental LEPR expression, which is in turn is associated with inflammatory markers in cord blood at birth. Further studies encompassing other aspects of leptin signalling are warranted to understand if these relationships are causal and have health implications.

Neonatal Sepsis Diagnosis Decision-Making Based on Artificial Neural Networks

Neonatal sepsis remains difficult to diagnose due to its non-specific signs and symptoms. Traditional scoring systems help to discriminate between septic or not patients, but they do not consider every single patient particularity. Thus, the purpose of this study was to develop an early- and late-onset neonatal sepsis diagnosis model, based on clinical maternal and neonatal data from electronic records, at the time of clinical suspicion. A predictive model was obtained by training and validating an artificial Neural Networks (ANN) algorithm with a balanced dataset consisting of preterm and term non-septic or septic neonates (early- and late-onset), with negative and positive culture results, respectively, using 25 maternal and neonatal features. The outcome of the model was sepsis or not. The performance measures of the model, evaluated with an independent dataset, outperformed physician's diagnosis using the same features based on traditional scoring systems, with a 93.3% sensitivity, an 80.0% specificity, a 94.4% AUROC, and a regression coefficient of 0.974 between actual and simulated results. The model also performed well-relative to the state-of-the-art methods using similar maternal/neonatal variables. The top 10 factors estimating sepsis were maternal age, cervicovaginitis and neonatal: fever, apneas, platelet counts, gender, bradypnea, band cells, catheter use, and birth weight.

Estimation of Neonatal Intestinal Perforation Associated with Necrotizing Enterocolitis by Machine Learning Reveals New Key Factors

Intestinal perforation (IP) associated with necrotizing enterocolitis (NEC) is one of the leading causes of mortality in premature neonates; with major nutritional and neurodevelopmental sequelae. Since predicting which neonates will develop perforation is still challenging; clinicians might benefit considerably with an early diagnosis tool and the identification of critical factors. The aim of this study was to forecast IP related to NEC and to investigate the predictive quality of variables; based on a machine learning-based technique. The Back-propagation neural network was used to train and test the models with a dataset constructed from medical records of the NICU; with birth and hospitalization maternal and neonatal clinical; feeding and laboratory parameters; as input variables. The outcome of the models was diagnosis: (1) IP associated with NEC; (2) NEC or (3) control (neither IP nor NEC). Models accurately estimated IP with good performances; the regression coefficients between the experimental and predicted data were R² > 0.97. Critical variables for IP prediction were identified: neonatal platelets and neutrophils; orotracheal intubation; birth weight; sex; arterial blood gas parameters (pCO₂ and HCO₃); gestational age; use of fortifier; patent ductus arteriosus; maternal age and maternal morbidity. These models may allow quality improvement in medical practice.

The association of ghrelin, leptin, and insulin levels in umbilical cord blood with fetal anthropometric measurements and glucose levels at birth

Objectives: To investigate the association of ghrelin, leptin, and insulin levels in the umbilical cord blood of the preterm and term infants with anthropometric measurements and glucose metabolism.Methods: Sixty-nine infants who were born between November 2004 and June 2005 were included in the study. Pregnancy ages, birth weights, heights, head circumferences, and Ponderal Indexes (PI) were identified. Ghrelin, leptin, insulin, and glucose levels in the umbilical cord blood were studied.Results: Eighteen infants out of 69 infants were preterm (34.6 ± 0.43 weeks), and 33 infants were term (38.7 ± 0.14 weeks). All preterm infant weights were appropriate for gestational age (AGA); 33 of the term infants' weights were AGA and 18 were large for gestational age (LGA). Leptin, insulin, and glucose levels of term infants were significantly higher compared with the preterm infants (p < .0001, p < .001, and p < .0001, respectively); no significant difference was detected in the ghrelin levels between the two groups (p > .05). The leptin and insulin levels of the term LGA infants were higher compared with the term AGA and preterm AGA infants (p < .05, for all). No difference was detected between the three groups regarding serum ghrelin levels (p > .05). No difference was found in the glucose levels between term AGA and LGA infants (p > .05); however, the serum glucose levels of term AGA and LGA infants were higher compared with levels in preterm AGA infants (p < .05, for both). A positive correlation was demonstrated in all study groups between leptin and insulin with gestational age, body weight, height, head circumference, and PI. A positive correlation was found between serum leptin levels with gestational age and insulin levels in preterm infants, and between serum leptin levels and insulin and glucose levels in term infants. No association was found between ghrelin and anthropometric measurements, leptin, insulin, and glucose levels (p > .05, for all).Conclusions: The increase of leptin production with increased gestational age, and the strong association with anthropometric measurements supports the opinion that leptin behaves as a fetal growth factor. Leptin in intrauterine life is in close association with insulin and glucose metabolism. Although ghrelin was at measurable levels in preterms, no association with fetal growth and glucose metabolism could be demonstrated in preterm and term infants.

Key Clinical Factors Predicting Adipokine and Oxidative Stress Marker Concentrations among Normal, Overweight and Obese Pregnant Women Using Artificial Neural Networks

Maternal obesity has been related to adverse neonatal outcomes and fetal programming. Oxidative stress and adipokines are potential biomarkers in such pregnancies; thus, the measurement of these molecules has been considered critical. Therefore, we developed artificial neural network (ANN) models based on maternal weight status and clinical data to predict reliable maternal blood concentrations of these biomarkers at the end of pregnancy. Adipokines (adiponectin, leptin, and resistin), and DNA, lipid and protein oxidative markers (8-oxo-2′-deoxyguanosine, malondialdehyde and carbonylated proteins, respectively) were assessed in blood of normal weight, overweight and obese women in the third trimester of pregnancy. A Back-propagation algorithm was used to train ANN models with four input variables (age, pre-gestational body mass index (p-BMI), weight status and gestational age). ANN models were able to accurately predict all biomarkers with regression coefficients greater than R² = 0.945. P-BMI was the most significant variable for estimating adiponectin and carbonylated proteins concentrations (37%), while gestational age was the most relevant variable to predict resistin and malondialdehyde (34%). Age, gestational age and p-BMI had the same significance for leptin values. Finally, for 8-oxo-2′-deoxyguanosine prediction, the most significant variable was age (37%). These models become relevant to improve clinical and nutrition interventions in prenatal care.