Search Results (33)

Preterm birth (PTB) refers to a labor before 37 gestational weeks. This is a major global contributor to neonatal morbidity and mortality. Although fetal sex is frequently treated as a confounding variable in PTB research, relatively few studies have conducted sex-stratified analyses to investigate how male and female fetuses may respond differently to various intrauterine exposures. This represents an underexplored area with important implications for understanding fetal sexual dimorphism-specific vulnerability to adverse pregnancy outcomes. Understanding the role of fetal sex differences in the pathophysiology of preterm birth (PTB) regarding processes such as inflammation, placental dysfunction, and oxidative stress is crucial. These delicate processes are tightly interrelated, but also independently contribute to pregnancy complications. Recognizing fetal sex as a biological variable for such processes is essential for improving mechanistic insight, providing refined predictive models. Full article

Preterm delivery affects approximately 10% of pregnancies worldwide and remains a major clinical challenge due to the lack of reliable early predictive tools. Existing strategies are often invasive, relying on blood or amniotic fluid samples and requiring complex processing. In this study, we describe a novel non-invasive approach based on the multiplex detection of inflammatory cytokines in small urine volumes from pregnant women. To account for clinical and temporal variability, we applied Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to adjust for gestational age at sampling and obstetric factors. Correlation network analyses revealed cytokine interactions that distinguished preterm from term deliveries, with macrophage-derived cytokines—MIP-1α, MIP-1β, IL-15, and IL-22—emerging as central nodes. These findings highlight the involvement of the IL-1 pathway in the pathophysiology of preterm labor. Furthermore, urinary IL-5 and IL-31 levels correlated positively with pregnancy duration, whereas IL-1β and IL-1Ra in urine and TNFα in amniotic fluid showed inverse associations. Altogether, this non-invasive methodology provides insight into immune dynamics during pregnancy and offers a foundation for future studies focused on biomarker discovery and mechanistic understanding of preterm birth. Full article

Objectives: Ritodrine, a tocolytic agent used to delay preterm labor, can cause several cardiovascular-associated adverse events (AEs). This study aimed to examine the relationship between gene polymorphisms in peroxisome proliferator-activated receptor gamma (PPARG) and PPARG coactivator-1α (PPARGC1A) and the occurrence of ritodrine-induced AEs. Additionally, a risk-scoring system was developed to identify patients at high risk of AEs. Methods: Patients aged 18 years or older who were administered ritodrine to manage preterm labor with intact membranes and uterine contractions occurring at 20–36 weeks of gestation were enrolled in this study. A total of 70 common PPARG and PPARGC1A variants (minor allele frequency ≥ 0.2) with low linkage disequilibrium (r² < 0.8) were selected from an Axiom™ Precision Medicine Research Array (AMPRA). Results: A total of 149 patients were included in the analysis. After adjusting for confounders (age, gestational age, and the maximum infusion rate), weight and rs2946385, rs35523565, and rs2240748 of PPARGC1A were identified as significant predictors associated with ritodrine-induced AEs. Based on the risk-scoring system, the predicted probabilities of AEs for patients with scores of 0, 1, 2, 3, 4, and 5 points were 4%, 9%, 18%, 35%, 55%, and 74%, respectively. The AUROC for the risk score predicting ritodrine-induced AEs was 0.729 (95% CI: 0.672–0.831, p < 0.001). Conclusions: This study indicates that ritodrine-induced AEs are related to PPARGC1A polymorphisms. A risk-scoring system based on genetic variants showed moderate predictive ability for ritodrine-induced AEs, suggesting potential utility in females with preterm labor. Full article

Background/objectives: Preterm labor is a leading cause of neonatal morbidity and mortality worldwide. Previous research has indicated that an inflammatory response or microbial invasion of the amniotic cavity is a pathological condition linked to preterm birth; hence, inflammatory markers such as metalloproteinase-9 (MMP-9), interleukin-6 (IL-6), and interleukin-8 (IL-8) have been utilized to predict preterm delivery. The identification of reliable biomarkers for early prediction is critical for improving maternal, fetal, and neonatal outcomes. Methods: To address this issue, a literature review has been conducted on PubMed/Medline and Scopus databases for articles investigating the possible correlation between IL6, IL8, and MMP9 and preterm labor. Results: Using a comprehensive search of the PubMed and Scopus databases, 12 studies were analyzed to identify the correlation between these biomarkers and preterm labor. Seven studies point the impact of increased IL-6 levels or polymorphisms of the gene and higher incidence of preterm labor. Two of the included studies identified the increased risk for preterm birth in elevated levels of IL-8 in amniotic fluid. Six studies highlight the increased incidence of preterm birth in women with polymorphisms of the MMP-9 gene. Conclusions: Elevated IL-6 levels and specific gene polymorphisms are strongly associated with preterm delivery risk, with IL-8 concentrations correlating with systemic inflammation and histologic chorioamnionitis. MMP-9 gene variations and protein levels showed significant predictive value for membrane rupture and labor onset. The findings emphasize integrating these biomarkers into diagnostic tools for routine prenatal care, enhancing early detection, risk stratification, and timely interventions to improve maternal and neonatal outcomes. Full article

Sonoelastography, a novel ultrasound-based technique, is emerging as a valuable tool in prenatal diagnostics by quantifying tissue elasticity and stiffness in vivo. This narrative review explores the application of sonoelastography in assessing maternal and fetal health, with a focus on cervical, placental, pelvic floor, and fetal tissue evaluations. In the cervix, sonoelastography aids in predicting preterm birth and assessing labor induction success. For the placenta, it provides insights into conditions like preeclampsia and intrauterine growth restriction through elasticity measurements. Assessing fetal tissues, including the lungs, liver, and brain, sonoelastography offers a non-invasive method for evaluating organ maturity and detecting developmental anomalies. Additionally, pelvic floor assessments enable better management of childbirth-related injuries and postpartum recovery. While current studies support its safety when used within established limits, further research is necessary to confirm long-term effects. Future advancements include refining protocols, integrating machine learning, and combining sonoelastography with other diagnostic methods to enhance its predictive power. Sonoelastography holds promise as an impactful adjunct to conventional ultrasound, providing quantitative insights that can improve maternal and fetal outcomes in prenatal care. Full article

Background/Objectives: Intra-amniotic infection (IAI) is a rare but serious condition with potential complications such as preterm labor and intrauterine fetal death. Diagnosing IAI is challenging due to varied clinical signs. Oxidative stress and mitochondrial dysfunction have been hypothesized to evolve around IAI. This study focused on measuring circulating mtDNA levels, a proposed biomarker for mitochondrial dysfunction, in maternal serum and placenta of women with confirmed IAI and healthy controls. Methods: 12 women with confirmed IAI (IAI group) were enrolled following premature preterm rupture of the membranes (PPROM) and compared to 21 healthy women (control group). Maternal blood was obtained two weeks pre-partum and peripartum; furthermore, postpartum placental blood was taken. In the IAI group, maternal blood was taken once weekly until delivery as well as peripartum, as was placental blood. Circulating cell-free mtDNA was quantified by real-time quantitative PCR. Results: Upon admission, in the IAI group, mean plasma mtDNA levels were 735.8 fg/μL compared to 134.0 fg/μL in the control group (p < 0.05). After delivery, in the IAI group, mean mtDNA levels in the placenta were 3010 fg/μL versus 652.4 fg/μL (p < 0.05). Conclusions: Circulating cell-free mtDNA could serve as a valuable biomarker for IAI prediction and diagnosis. Future research should establish reference values for sensitivity in predicting IAI. Full article

Vasa previa is a pregnancy complication that occurs when unprotected fetal blood vessels traverse the cervical os, placing the fetus at high risk of exsanguination and fetal death. These fetal vessels may be compromised by fetal movement and compression, leading to poor oxygen distribution and asphyxiation. Diagnostic tools for vasa previa management and preterm labor (PTL) include transvaginal ultrasound, cervical length (CL) surveillance and use of fetal fibronectin (FFN) testing. These tools can prove to be quite useful as they allow for lead time in the prediction of PTL and spontaneous rupture of membranes which can result in devastating outcomes for pregnancies affected by vasa previa. We conducted a literature review on vasa previa management and the usefulness of FFN and CL surveillance in predicting PTL and found 36 related papers. Although there is limited research available to show the impact of FFN and CL surveillance in the management of vasa previa, there is sufficient evidence to support FFN and CL surveillance in predicting the onset of PTL, which can have devastating consequences for the pregnancies affected. It can be extrapolated that these tools, by helping to determine pregnancies at risk for PTL, could improve management and outcomes in patients with vasa previa. Future studies investigating the management of vasa previa with FFN and CL surveillance to reduce the burden of PTL and its associated comorbidities are warranted. Full article

This systematic review delves into the connections between microRNAs and preterm labor, with a focus on identifying diagnostic and prognostic markers for this crucial pregnancy complication. Covering studies disseminated from 2018 to 2023, the review integrates discoveries from diverse pregnancy-related scenarios, encompassing gestational diabetes, hypertensive disorders and pregnancy loss. Through meticulous search strategies and rigorous quality assessments, 47 relevant studies were incorporated. The synthesis highlights the transformative potential of microRNAs as valuable diagnostic tools, offering promising avenues for early intervention. Notably, specific miRNAs demonstrate robust predictive capabilities. In conclusion, this comprehensive analysis lays the foundation for subsequent research, intervention strategies and improved outcomes in the realm of preterm labor. Full article

The effective prediction of preterm labor continues to be a topic of interest for research within pregnancy medicine, where uterine muscle contraction signals have shown to be insightful to predict a potential preterm birth. Magnetomyography (MMG) is a physiological-measurement-based tool which measures the orthogonal offset of bioelectrical manifestations from uterine contractions and may serve to predict potential premature deliveries with an enhanced accuracy. The decoding of the physiological signal is an area of substantial research where classical signal processing approaches and metaheuristics optimization routines have been utilized in the postprocessing and decomposition of MMG signals. This work requires a degree of expert knowledge and an understanding of tuning and parameter initialization. As a stride towards creating a more automated clinical decision support platform for predictions of preterm labor, we employ the use of a deep wavelet scattering (DWS) model. This methodology allows for a deep multiresolution analysis alongside unsupervised feature learning for the postprocessing of candidate MMG signals. DWS is combined with select pattern-recognition-based prediction machines in order to assemble a clinical decision pipeline for the prediction of the states of various pregnancies, with a greater degree of machine intelligence. The patient cohort consisted of a multi-ethnic demographic population composed of preterm and term pregnancies, where births occurred both under and over 48 h after labor commenced. Contrasting results were found between the various methods from the literature and DWS using the logistic regression algorithm. It was seen that DWS produced a slightly lower accuracy in comparison, as a trade-off for its streamlined unsupervised feature extraction process. Further work will now involve the application of various other machine learning methods in an attempt to assess and identify the most appropriate machine learning method with DWS that proves to be the most accurate. Full article

We conducted a prospective double-blind study to compare two vaginal diagnostic methods in singleton pregnancies with threatened preterm labor (TPL) at the University Hospital of Clermont-Ferrand (France) from August 2018 to December 2020. Our main objective was to compare the diagnostic capacity at admission, in terms of positive predictive value (PPV) and negative predictive value (NPV), of Premaquick^® (combined detection of IL-6/total IGFBP-1/native IGFBP-1) and QuikCheck fFN™ (fetal fibronectin) for delivery within 7 days in cases of TPL. We included 193 patients. Premaquick^® had a sensitivity close to 89%, equivalent to QuikCheck fFN™, but a higher statistical specificity of 49.5% against 38.6% for QuikCheck fFN™. We found no superiority of Premaquick^® over QuickCheck fFN™ in terms of PPV (6.6% vs. 7.9%), with NPV being equivalent in predicting childbirth within 7 days in cases of TPL (98.6% vs. 98.9%). Nevertheless, the combination of positive native and total IGFBP-1 and the combination of all three positive markers were associated with a higher PPV. Our results, though non-significant, support this combined multiple-biomarker approach to improve testing in terms of predictive values. Full article

Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother’s mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks. Full article

Premature birth is a worldwide health issue, posing a high mortality risk for newborns, as well as causing emotional and financial difficulties, and long-term health issues for patients. Identifying effective predictors for preterm birth is essential for prolonging gestation or improving obstetric care. As invasive methods are costly, risky, and not universally available, we aim to assess the predictive capacity of various serum parameters in pregnant women during the third trimester, as a non-invasive alternative. Based on previous studies, it was hypothesized that hemoglobin, the association of hemoglobin, albumin, lymphocyte, and platelets’ (HALP) score, and coagulation parameters such as the prothrombin time (PT), activated partial thromboplastin clotting time (aPTT), D-dimers, and fibrinogen to albumin ratio (FAR) have significant prediction capabilities. With a retrospective design, a total of 161 patients with a history of preterm birth were included in the analysis, being matched 1:1 with a control group of women who gave birth at term. All laboratory samples were collected during the third trimester of pregnancy. The computed area under the curve (AUC) ranged between 0.600 and 0.700 in all six studied parameters, suggesting a fair discrimination. The highest predictive value for preterm birth was observed to be represented by the HALP score with AUC = 0.680 and the highest sensitivity (75%, p-value = 0.001). The highest specificity was achieved by the prothrombin time (69%), and the HALP score was also 69%. The FAR score had an AUC of 0.646, with a sensitivity of 68%, and specificity of 64% (p-value = 0.020). All other variables were significant estimates for the risk of preterm birth, although with lower accuracy. Pregnant women with a hemoglobin level below 12.0 g/dL had a 3.28 higher likelihood of giving birth prematurely. A prothrombin time below 12.5 s determined a 2.11 times higher risk of preterm birth. Similarly, the aPTT below 25 s was linked with 3.24 higher odds of giving birth prematurely. However, the strongest predictors were the D-dimers above 250 ng/mL (OR = 4.26), the FAR score below 0.1, with an odds ratio of 5.30, and the HALP score with a 6.09 OR for a cut-off value above 24. It is important to determine these parameters in pregnant women at risk for giving birth prematurely, but further external validation is required to confirm these findings. Full article

Prematurity comes with a varying range of complications, implying a high prevalence of complications and mortality and depending on the severity of prematurity and the sustained inflammation among these infants, which recently sparked an important scientific interest. The primary objective of this prospective study was to establish the degree of inflammation in very (VPIs) and extremely preterm infants (EPIs) in association with the histology findings of the umbilical cord (UC), while the secondary objective was to study the inflammatory markers in the neonates’ blood as predictors of fetal inflammatory response (FIR). A total of thirty neonates were analyzed, ten of them being born extremely premature (<28 weeks of gestation) and twenty very premature (28–32 weeks of gestation). The EPIs had considerably higher levels of IL-6 at birth than VPIs (638.2 pg/mL vs. 151.1 pg/mL). The CRP levels at delivery did not vary substantially across groups; however, after days, the EPIs had significantly higher CRP levels (11.0 mg/dL vs. 7.2 mg/dL). In contrast, the LDH was considerably higher in the extremely preterm infants at birth and four days after birth. Surprisingly, the proportions of infants with pathologically increased inflammatory markers did not differ between the EPIs and VPIs. The LDH increased considerably in both groups, although the CRP levels increased exclusively among the VPIs. The stage of inflammation in the UC did not vary substantially between the EPIs and VPIs. The majority of infants were identified with Stage 0 UC inflammation (40% in EPI vs. 55% in VPIs). There was a substantial correlation link between gestational age and newborn weight and a significant inverse correlation among gestational age and IL-6 and LDH levels. There was a strong negative association between weight and IL-6 (rho = −0.349) and LDH (rho = −0.261). The stage of the UC inflammation demonstrated a statistically significant direct connection with IL-6 (rho = 0.461) and LDH (rho = 0.293), but none with the CRP. Further studies involving a bigger population size of preterm newborns are required to validate the findings and analyze more inflammatory markers, while prediction models on inflammatory markers that are measured expectantly, before the onset of preterm labor, need to be created. Full article

In many countries, preterm birth, defined as birth before 37 completed weeks of gestation, is the primary cause of infant death and morbidity. An increasing body of research suggests that inflammation (both clinical and subclinical) plays a significant role in inducing preterm labor or developing pregnancy problems that lead to premature birth. Consequently, the purpose of this research was to determine the predictive value of the Neutrophil-Lymphocyte Ratio (NLR), derived Neutrophil-Lymphocyte Ratio (dNLR), Monocytes-to-Lymphocyte Ratio (MLR), Platelets-to-Lymphocyte Ratio (PLR), Systemic immune-inflammation index (SII), and systemic inflammatory response index (SIRI), for premature delivery. A retrospective study analyzed a total of 243 eligible pregnancies that resulted in a preterm birth during 2020 and 2021. A control group without a history of preterm birth was matched by age and trimester of laboratory analysis at a 1:1 ratio. Although the number of comorbidities was similar among study groups, the body-mass index estimated for the week of gestation was significantly higher among the patients from the prematurity group, as well as the prevalence of urinary tract infections and smoking. Laboratory data showed that patients with a preterm birth had significantly higher white blood cell count and monocytes, but significantly lower lymphocytes, platelets, and hemoglobin. The NLR, dNLR, PLR, and MLR scores showed to be significantly higher among patients from the prematurity group, but SII and SIRI were not significantly different between the study groups. It was observed that the AUC values of NLR, dNLR, PLR, and MLR were higher than 0.600, respectively NLR had the highest value among the tested scores (AUC = 0.694) and the highest sensitivity in this study (71%). The highest sensibility was achieved by dNLR, with 70%, and an AUC value of 0.655 (p-value = 0.022). PLR had the second-highest AUC value (0.682) and the best score in terms of sensitivity (70%) and sensibility (69%) (p-value = 0.015). Lastly, MLR had the lowest significant AUC score (0.607) and lowest sensitivity/sensibility. The significant cut-off values for the inflammatory scores were 9.0 for NLR, 9.8 for dNLR, 250 for PLR, and 4.07 for MLR. After evaluating the importance of these inflammatory scores, further clinical applications should be conducted to confirm the results and improve therapy and care to reduce the burden of premature deliveries. Full article

Preterm pregnancies are one of the leading causes of morbidity and mortality amongst children under the age of five. This is a global issue and has been identified as an area requiring active research. The emphasis now is to identify and develop methods of predicting the likelihood of preterm birth. This paper uses physiological data from a group of patients in active labor. The dataset contains information about fetal heart rate (FHR) and maternal heart rate (MHR) for all patients and electrohysterogram (EHG) recordings for the measurement of uterine contractions. For the physiological data analysis and associated signal processing, we utilize deep wavelet scattering (DWS). This is an unsupervised decomposition and feature extraction method combining characteristics from deep learning convolutions, as well as the classical wavelet transform, to observe and investigate the extent to which active preterm labor can be accurately identified from an acquired physiological signal, the results of which were compared with the metaheuristic linear series decomposition learner (LSDL). Additional machine learning algorithms are tested on the acquired physiological data to allow for the identification of optimal model architecture for this specific physiological data. Full article