Estimation Of Birth Weight Research Articles

Urinary Neutrophil Gelatinase-Associated Lipocalin Values in Preterm Neonates: A Systematic Review and Meta-analysis.

Acute kidney injury (AKI) is common in hospitalized preterm neonates. Urinary neutrophil gelatinase-associated lipocalin (uNGAL) is a promising noninvasive AKI biomarker. However, normal values of uNGAL in preterm neonates without AKI are poorly characterized. The objective of this study was to evaluate the current literature to determine normal uNGAL values for preterm neonates without AKI. Systematic review and meta-analysis of all articles published before November 2021 evaluating uNGAL values in preterm neonates without AKI. Of 1,607 studies evaluated for eligibility, 11 were included in the final meta-analysis (210 males, 202 females). uNGAL values were higher in the preterm neonates <29 weeks and ranged between 20.7 and 782.65 ng/mL. Meta mean estimates of gestational age (GA), birthweight, and neutrophil gelatinase-associated lipocalin were 29.4 weeks (95% confidence interval [CI]: 28.8-30.0), 1,241 g (95% CI: 1,111-1,372), and 148.9 ng/mL (95% CI: 48-231), respectively. In limited studies, a wide range of uNGAL values in preterm neonates without AKI are reported. Future studies should identify normal uNGAL values in preterm neonates using larger cohorts by GA and birthweight. · Urinary NGAL is a promising noninvasive biomarker of neonatal AKI.. · A wide range of uNGAL is reported in preterm neonates but the baseline values are not well defined.. · Urine NGAL values are higher in extremely preterm compared with preterm neonates..

The Azawak zebu bred at the Toukounous experimental centre in Niger: reproduction performance and estimation of genetic parameters.

The aim of this study was to estimate the genetic parameters of the reproductive performance of the Azawak zebu, bred on station during the period 1981 to 2007, in order to contribute to the implementation of an effective selection programme for improving the zootechnical performance of cattle herds. The data contained records of 2453 calvings from 650 Azawak cows, daughters of 55 bulls, all bred at the Toukounous experimental centre (Niger). Non-genetic factors were evaluated using linear mixed models and genetic parameters were estimated using REML procedures with a multi-trait animal model. The effects of year and calving rank were significant (P < 0.001) for the three reproductive traits studied. Heritability was low for age at first calving (h2 = 0.09 ± 0.03) and calf birth weight (h2 = 0.07 ± 0.03). It was moderate for calving interval (h2 = 0.30 ± 0.04) indicating the possibility of genetic improvement of this trait by selection. Repeatability estimates for calf birth weight and calving interval were 0.10 ± 0.02 and 0.42 ± 0.03 respectively. Calving interval had very strong and significant genetic correlations with age at first calving (0.84 ± 0.08) and calf birth weight (-0.40 ± 0.20). There was also a significantly favourable genetic correlation between calf weight at birth and age at first calving (-0.32 ± 0.25), implying a simultaneous improvement in all three reproductive traits through selection. An increase in heritability of calf birth weight was observed after the active phase of the program, reflecting an overly open selection scheme. These genetic parameter estimates suggest that the three reproductive traits could be used in selection index with the aim to improve the three reproductive traits of Azawak cattle.

Accurate birth weight prediction from fetal biometry using the Gompertz model

ObjectivesMonitoring of fetal growth and estimation of birth weight is of clinical importance. During pregnancy, ultrasound fetal biometry values including femur length, head circumference, abdominal circumference, biparietal diameter are measured and used to place fetuses on “growth charts”. There is no simple growth-model-based, predictive formula in use for fetal biometry. Estimation of fetal weight at birth currently depends on ultrasound data taken a short time before birth. Study designOur cohort (“Seethapathy cohort”) consists of ultrasound biometry measurements and other data for 774 pregnant women in Chennai, India, 2015–2017. We use the Gompertz model, a standard model for constrained growth, with just three intuitive parameters, to model the growth of fetal biometry, and a machine learning (ML) model trained on these parameters to predict birth weight (BW). ResultsThe Gompertz model convincingly fits the growth of fetal biometry values. Two Gompertz parameters—t0 (inflection time) and c (rate of decrease of growth rate)—seem universal to all fetuses, while the third, A, is an overall scale specific to each fetus, capturing individual variation. On the Seethapathy cohort we can infer A for each fetus from ultrasound data available by the 24 or 35 weeks. Our ML model predicts birth weight with < 8 % error, outperforming published methods that have access to late-term ultrasound data. The same model gives an 8.4 % error in BW prediction on an independent validation cohort of 365 women. ConclusionsThe Gompertz model fits fetal biometry growth and enables birth weight estimation without need of late-term ultrasounds. Aside from its clinical predictive value, we suggest its use for future growth standards, with almost all variation described by a single scale parameter A.

Predicting the fetal weight by ultrasonography for isolated polyhydramnios: Comparison of 14 formulas.

To recalculate the estimated fetal weight (EFW) based on ultrasound measurements in patients complicated with isolated polyhydramnios, using 14 current formulas to observe which formula better predicts the EFW. This study examined pregnant women who gave birth in the hospital between January 2015 and January 2020. Maximum vertical pocket (MVP) was classified as, mild, moderate, and severe polyhydramnios, and the patients' measurements were reanalyzed using 14 formulas. The estimation of birth weight (EBW) alongside observed birth weight (OBW) facilitated the computation of statistical indices, namely the mean absolute percentage error (MAPE) expressed as [(EBW - OBW)/OBW × 100], the mean percentage error (MPE) denoted as (EBW - OBW)/(OBW × 100), and their corresponding 95% confidence intervals. A total of 564 polyhydramnios patients were included in the study. When looking at the MAPE, the lowest rate (7.65) was found in the Hadlock 2 formula. Hadlock 1, Hadlock 3, and Shinozuka formulas demonstrated MAPE values most closely aligned with Hadlock 2. Weiner I and Thurnau were the formulas with the highest MAPE values. When the cut-off values for MAPE were taken as 10%, 4/14 of the formulas (Weiner I-II, Vintzleos and Thurnau) gave results above 10%. Among 14 formulas, 3 (21.4%) had positive (sonographic overestimation) (Hadlock 3, Shinozuka, and Vintzleos) and the other 11 (78.6%) had negative MPE (sonographic underestimation). The Hadlock 2 formula had the lowest MAPE in predicting birth weight in patients with polyhydramnios, closely followed by the Hadlock 1, Hadlock 3, and Shinozuka formulas.

Assessing the diagnostic accuracy of postnatal clinical scoring methods and foot length measurement for estimating gestational age and birthweight of newborns in low- and middle-income countries: a systematic review and meta-analysis

BackgroundThis study aimed to update systematic reviews and meta-analyses on the diagnostic accuracy of postnatal clinical scoring (PCS) methods and foot length (FL) measurement for assessing gestational age (GA) and...

Estimation of Genetic Parameters of Early Growth Traits in Dumeng Sheep.

This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits.

Peripartum Prediction of Fetal Weight in Gravidas With Obesity.

Estimated fetal weight (EFW) is an important metric at delivery as neonates with abnormal birthweight and their mothers are at higher risk of birth complications. Data regarding optimal EFW assessment in gravidas with obesity is inconsistent, and with the increasing incidence of obesity, clarification of this question is crucial. We aimed to compare accuracy of ultrasound (US)-derived EFW and clinical assessments of EFW in predicting neonatal birthweight among gravidas with obesity. This prospective cohort study enrolled gravidas with obesity and a singleton pregnancy admitted for delivery at term. EFW was determined using either US biometry or clinical assessment (Leopold's maneuvers, Johnson's formula, and Insler's formula) at time of admission. Our primary outcome was accurate EFW, defined as EFW within 500 g of birthweight. Secondary outcomes included ability to predict small-for-gestational age (SGA) and large-for-gestational age (LGA) birthweights. These outcomes were compared between all EFW methods. A total of 250 gravidas with a median body mass index of 36.4 kg/m2 were enrolled. Admission US outperformed Leopold's maneuvers in obtaining accurate EFW (81.6% versus 74.5%, P = .03). When comparing all methods, Johnson's and Insler's formulae performed the worst, accurately predicting EFW in only 27.4% and 14.3% of cases, respectively. Likewise, US-derived EFW outperformed Leopold's maneuvers and fundal height in the prediction of SGA and LGA neonates. US is more accurate than clinical assessment of EFW in gravidas with obesity both for estimation of actual birthweight and prediction of abnormal birthweight. Universal late third-trimester or peripartum US for EFW should be considered in gravidas with obesity.

Genetic parameter estimates for growth traits in Texel sheep of Brazil

The present study aimed to estimate the genetic parameters for direct and maternal influences on birth weight (BW), weaning weight (WW) and postweaning weight (PW) of Texel sheep, raised in an extensive system from Brazil. Data file used in this study consisted of 834 body weight records of 834 Texel lambs (376 males and 458 females) born from 2020 to 2021, offspring of 475 ewes and 45 rams. Multivariate animal model was conducted to estimate the variance components, heritability and genetic correlations using the restricted maximum likelihood method in ASReml-R software, while the effects of fixed factors were analyzed by the general linear model procedure of statistical analysis system. Five different animal models, with or without maternal genetic effect, maternal permanent environment effect and covariance between additive genetic and maternal genetic effects, were fitted on the growth traits data of the Texel sheep population. AIC and BIC test results were used to evaluate the best fit models for growth traits. The effect of lamb's sex was significant on BW, WW and PW while birth type and year of lambing effects were on all traits (P < 0.05). Model 4, incorporating direct additive genetic, maternal additive genetic and maternal permanent environmental effects showed the minimum AIC estimate for all growth traits. The direct additive genetic heritability for BW, WW and PW were 0.11 ± 0.03, 0.37 ± 0.05 and 0.31 ± 0.08, respectively and maternal heritability for these traits were 0.00 ± 0.00, 0.04 ± 0.02 and 0.00 ± 0.00, respectively. The total heritability estimates for BW, WW and PW were 0.11 ± 0.03, 0.39 ± 0.05 and 0.31 ± 0.08, respectively. The estimates of the direct additive genetic correlation between BW-WW, BW-PW and WW-PW were 0.68 ± 0.12, 0.66 ± 0.12 and 0.78 ± 0.07, respectively. The maternal permanent environmental correlation between BW-WW, BW-PW and WW-PW were 0.38 ± 0.27, 0.29 ± 0.20 and 0.74 ± 0.16, respectively. Phenotypic correlation ranged from 0.43 ± 0.03 to 0.75 ± 0.02. It is concluded that due to the existence of genetic variation for BB, WW and PW and strong genetic correlation among them, genetic progress for these traits is possible by selection in Texel sheep from Brazil.

Direct and maternal genetic parameters for growth traits in Jersey crossbred cattle

Abstract Growth data on Jersey crossbred calves, maintained at ICAR-National Dairy Research Institute, Eastern Regional Station, Kalyani, Nadia, West Bengal, India, were collected and analysed to assess the influence of maternal effects on growth traits of calves. Traits considered for this study were birth weight (BW) and weights at 3 months (W3M), 6 months (W6M), 9 months (W9M) and 12 months (W12M) of age. Least-squares analyses were employed to obtain the effects of non-genetic factors on the traits of interest. Determination of influence of maternal effects on growth traits was estimated by fitting three univariate animal models (including or excluding maternal effects) using Bayesian approach. The most appropriate model for each trait was selected based on Deviance Information Criterion. Direct heritability (h2) estimates for BW, W3M, W6M, W9M and W12M were 0.31 ± 0.08, 0.26 ± 0.10, 0.48 ± 0.10, 0.44 ± 0.11 and 0.39 ± 0.14, respectively, under the best model. Permanent environmental maternal effects (c2) varied from 0.04 to 0.12 for all traits. Existence of maternal effects for all ages reflects the importance of maternal components for these traits. Moderate to high heritability estimates for growth traits indicate the possibility of modest genetic progress for these traits through selection under prevalent management system.

Comparing Fetal Ultrasound Biometric Measurements to Neonatal Anthropometry at the Extremes of Birth Weight.

Error in birthweight prediction by sonographic estimated fetal weight (EFW) has clinical implications, such as avoidable cesarean or misclassification of fetal risk in labor. We aimed to evaluate optimal timing of ultrasound and which fetal measurements contribute to error in fetal ultrasound estimations of birth size at the extremes of birthweight. We compared differences in head circumference (HC), abdominal circumference (AC), femur length, and EFW between ultrasound and corresponding birth measurements within 14 (n = 1,290) and 7 (n = 617) days of birth for small- (SGA, <10th percentile), appropriate- (AGA, 10th-90th), and large-for-gestational age (LGA, >90th) newborns. Average differences between EFW and birthweight for SGA neonates were: -40.2 g (confidence interval [CI]: -82.1, 1.6) at 14 days versus 13.6 g (CI: -52.4, 79.7) at 7 days; for AGA, -122.4 g (-139.6, -105.1) at 14 days versus -27.2 g (-50.4, -4.0) at 7 days; and for LGA, -242.8 g (-306.5, -179.1) at 14 days versus -72.1 g (-152.0, 7.9) at 7 days. Differences between fetal and neonatal HC were larger at 14 versus 7 days, and similar to patterns for EFW and birthweight, differences were the largest for LGA at both intervals. In contrast, differences between fetal and neonatal AC were larger at 7 versus 14 days, suggesting larger error in AC estimation closer to birth. Using a standardized ultrasound protocol, SGA neonates had ultrasound measurements closer to actual birth measurements compared with AGA or LGA neonates. LGA neonates had the largest differences between fetal and neonatal size, with measurements 14 days from delivery showing 3- to 4-fold greater differences from birthweight. Differences in EFW and birthweight may not be explained by a single fetal measurement; whether estimation may be improved by incorporation of other knowable factors should be evaluated in future research. · Ultrasound measurements may be inadequate to predict neonatal size at birth.. · Birthweight estimation error is higher for neonates >90th percentile.. · There is higher error in AC closer to birth..

Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis

BackgroundCurrent guidelines regarding oxytocin stimulation are not tailored to individuals as they are based on randomised controlled trials. The objective of the study was to develop an artificial intelligence (AI) model for individual prediction of the risk of caesarean delivery (CD) in women with a cervical dilatation of 6 cm after oxytocin stimulation for induced labour. The model included not only variables known when labour induction was initiated but also variables describing the course of the labour induction.MethodsSecondary analysis of data from the CONDISOX randomised controlled trial of discontinued vs. continued oxytocin infusion in the active phase of induced labour. Extreme gradient boosting (XGBoost) software was used to build the prediction model. To explain the impact of the predictors, we calculated Shapley additive explanation (SHAP) values and present a summary SHAP plot. A force plot was used to explain specifics about an individual’s predictors that result in a change of the individual’s risk output value from the population-based risk.ResultsAmong 1060 included women, 160 (15.1%) were delivered by CD. The XGBoost model found women who delivered vaginally were more likely to be parous, taller, to have a lower estimated birth weight, and to be stimulated with a lower amount of oxytocin. In 108 women (10% of 1060) the model favoured either continuation or discontinuation of oxytocin. For the remaining 90% of the women, the model found that continuation or discontinuation of oxytocin stimulation affected the risk difference of CD by less than 5% points.ConclusionIn women undergoing labour induction, this AI model based on a secondary analysis of data from the CONDISOX trial may help predict the risk of CD and assist the mother and clinician in individual tailored management of oxytocin stimulation after reaching 6 cm of cervical dilation.

Effects of Maternal Anthropometry on Infant Anthropometry: A Cross-sectional Study at Public Hospital X in Ternate, Indonesia

Infant anthropometry is an indicator of neonatal survival. This study aimed to determine the effects of maternal anthropometry on estimating infant anthropom­etry. This cross-sectional study on 173 pregnant women at Public Hospital X in Ternate, Indonesia, was conducted from August 2018 to March 2023. The el­igible criteria were pregnant women aged ≥18 years, single pregnancy, and antenatal care (ANC) visits to the same hospital. The variables used included ma­ternal anthropometric measurements (body weight, body height, third-trimester weight (TTW)), gestational weight gain (GWG), education, age, ANC visits, and gestational age at delivery (GAD). A logistic regression model was employed to estimate significant variables related to infant anthropometric measure­ments (birth weight, birth length, and head circumference). The results showed that TTW, GWG, ANC, and GAD had significantly affected birth weight. Bodyweight, height, and TTW also significantly affected birth length. In addition, only GAD significantly affected the head circumference. In multivariate analysis, TTW and GWG significantly affected birth weight. Furthermore, only body height and GAD affected the birth length. Maternal anthropometrics become impor­tant indicators for estimating birth weight and birth length.

A Systematic Review on the Applications of Machine Learning for Fetal Birth Weight Prediction

In order to protect the maternal and infant safety, birth weight is an important indicator during fetal development. A doctor's experience in clinical practice, however, helps estimate birth weight by using empirical formulas based on the experience of the doctors. Recently, birth weights have been predicted using machine learning (ML) technologies. A machine learning model is built on the basis of a collection of attributes learns to predict predefined characteristics or results. Using a machine learning model, input and output are modeled together and then a set of models are trained on the data. It is possible to use machine learning for a variety of tasks such as predicting risks, diagnosing diseases, and classifying objects due to its scalability and flexibility, which are advantages over conventional methods. This research reviews the machine learning classification models used previously by various researchers to predict fetal weight. In this paper 85 studies were reviewed. Machine learning approach was considered as a better option to predict the fetal weight in all the studies included in this paper. The findings of this research show that the accuracy rate of using machine learning applications for fetal birth weight prediction is above 60% in all the studies reviewed.

National, regional, and global estimates of low birthweight in 2020, with trends from 2000: a systematic analysis

Low birthweight (LBW; <2500 g) is an important predictor of health outcomes throughout the life course. We aimed to update country, regional, and global estimates of LBW prevalence for 2020, with trends from 2000, to assess progress towards global targets to reduce LBW by 30% by 2030. For this systematic analysis, we searched population-based, nationally representative data on LBW from Jan 1, 2000, to Dec 31, 2020. Using 2042 administrative and survey datapoints from 158 countries and areas, we developed a Bayesian hierarchical regression model incorporating country-specific intercepts, time-varying covariates, non-linear time trends, and bias adjustments based on data quality. We also provided novel estimates by birthweight subgroups. An estimated 19·8 million (95% credible interval 18·4-21·7 million) or 14·7% (13·7-16·1) of liveborn newborns were LBW worldwide in 2020, compared with 22·1 million (20·7-23·9 million) and 16·6% (15·5-17·9) in 2000-an absolute reduction of 1·9 percentage points between 2000 and 2020. Using 2012 as the baseline, as this is when the Global Nutrition Target began, the estimated average annual rate of reduction from 2012 to 2020 was 0·3% worldwide, 0·85% in southern Asia, and 0·59% in sub-Saharan Africa. Nearly three-quarters of LBW births in 2020 occurred in these two regions: of 19 833 900 estimated LBW births worldwide, 8 817 000 (44·5%) were in southern Asia and 5 381 300 (27·1%) were in sub-Saharan Africa. Of 945 300 estimated LBW births in northern America, Australia and New Zealand, central Asia, and Europe, approximately 35·0% (323 700) weighed less than 2000 g: 5·8% (95% CI 5·2-6·4; 54 800 [95% CI 49 400-60 800]) weighed less than 1000 g, 9·0% (8·7-9·4; 85 400 [82 000-88 900]) weighed between 1000 g and 1499 g, and 19·4% (19·0-19·8; 183 500 [180 000-187 000]) weighed between 1500 g and 1999 g. Insufficient progress has occurred over the past two decades to meet the Global Nutrition Target of a 30% reduction in LBW between 2012 and 2030. Accelerating progress requires investments throughout the lifecycle focused on primary prevention, especially for adolescent girls and women living in the most affected countries. With increasing numbers of births in facilities and advancing electronic information systems, improvements in the quality and availability of administrative LBW data are also achievable. The Children's Investment Fund Foundation; the UNDP-UNFPA-UNICEF-WHO World Bank Special Programme of Research, Development and Research Training in Human Reproduction; and the Bill & Melinda Gates Foundation.

Duration of residence and offspring birth weight among foreign-born mothers in Spain: a cross-sectional study.

Duration of residence has been used to monitor changes in the health of a foreign-born population in a destination country. This study assesses whether the mother's duration of residence influences the relationship between maternal origin and birth weight. We conducted a cross-sectional study using Spanish census microdata (2011) linked to Vital Statistics (2011-15). Linear and multinomial logistic regression models were used to estimate birth weight differences between children born to foreign-born mothers by duration of residence and those born to natives. Maternal countries of origin were classified according to the Human Development Index (HDI). Our findings revealed significant differences in birth weight of 109683 births from both foreign- and native-born mothers. Overall, in descriptive statistics, compared with Spanish mothers, foreign-born mothers gave birth more frequently to high-birth weight (HBW) newborns (8.4% vs. 5.3%, respectively) and less frequently to low-birth weight (LBW) newborns (4.8% vs. 5.1%). According to the model's estimations, the risk of giving birth to HBW babies remains relatively high in foreign-born mothers. Especially, mothers from very high-HDI countries experienced changes in the RRR of HBW (1.59-1.28) and LBW (0.58-0.89) after spending over 10 years in Spain. Foreign-born mothers residing in Spain are at increased risk of delivering a HBW child regardless of their duration of residence. In fact, given the long-term health consequences associated with HBW, our results highlight the need to improve prenatal care in the foreign-born population.

Ultrasound evaluation of the fetal fat tissue, heart, liver and umbilical cord measurements in pregnancies complicated by gestational and type 1 diabetes mellitus – Potential application in the fetal birth-weight estimation and prediction of the fetal macrosomia

Ultrasound evaluation of the fetal fat tissue, heart, liver and umbilical cord measurements in pregnancies complicated by gestational and type 1 diabetes mellitus – Potential application in the fetal birth-weight estimation and prediction of the fetal macrosomia

Assessment of The Accuracy of Incorporation of Fetal Thigh Circumference in The Formula of Estimating Birth weight Using Two-Dimensional Ultrasound

Assessment of The Accuracy of Incorporation of Fetal Thigh Circumference in The Formula of Estimating Birth weight Using Two-Dimensional Ultrasound

Correlation of Cross-sectional Area of the Umbilical Cord on Antenatal Ultrasound with Neonatal Birth Weight in Pregnant Women with and without Gestational Diabetes Mellitus: A Prospective Cohort Study

Introduction: Many foetal and maternal complications, such as polyhydramnios, macrosomia, birth injuries, and operative interference, are linked with Gestational Diabetes Mellitus (GDM). Among these, macrosomia is particularly significant, as it increases the risk of shoulder dystocia, brachial plexus injury, birth asphyxia, and maternal complications, including emergency Lower Segment Caesarean Section (LSCS), Postpartum Haemorrhage (PPH), and perineal trauma. Therefore, one of the most essential perinatal goals in GDM is to predict macrosomia by estimating birth weight, thereby preventing adverse maternal and foetal outcomes. Aim: To evaluate whether there is a correlation between the cross-sectional area of the Umbilical Cord (UCA) and the Neonatal Birth Weight (NBW) in pregnant women with GDM. Materials and Methods: A prospective cohort study was conducted in the Departments of Radiology and Obstetrics Bangalore Medical College, Bengaluru, Karnataka, India. A total of 100 pregnant women (50 with GDM and 50 without GDM) were recruited from July 2021 to July 2022. Ultrasound examination (USG) was performed on pregnant women after 36 weeks of gestation. The UCA was measured in a free-floating loop, and the NBW was measured using a digital scale. The correlation coefficient was calculated to determine the degree of correlation between UCA and NBW. Results: The mean age of the subjects was 26.5±3.9 years. The frequency of macrosomia was higher in the GDM group (8%) compared to the control group (4%). A strong positive correlation was observed between UCA and NBW in both diabetics (r=0.819) and control groups (r=0.736). Conclusion: A strong positive correlation exists between UCA and NBW in women with GDM. Therefore, it should be estimated during routine antenatal USG for the prediction of birth weight in such women.

Bayesian rank likelihood-based estimation: An application to low birth weight in Ethiopia.

Low birth weight is a significant risk factor associated with high rates of neonatal and infant mortality, particularly in developing countries. However, most studies conducted on this topic in Ethiopia have small sample sizes, often focusing on specific areas and using standard models employing maximum likelihood estimation, leading to potential bias and inaccurate coverage probability. This study used a novel approach, the Bayesian rank likelihood method, within a latent traits model, to estimate parameters and provide a nationwide estimate of low birth weight and its risk factors in Ethiopia. Data from the Ethiopian Demographic and Health Survey (EDHS) of 2016 were used as a data source for the study. Data stratified all regions into urban and rural areas. Among 15, 680 representative selected households, the analysis included complete cases from 10, 641 children (0-59 months). The evaluation of model performance considered metrics such as the root mean square error, the mean absolute error, and the probability coverage of the corresponding 95% confidence intervals of the estimates. Based on the values of root mean square error, mean absolute error, and probability coverage, the estimates obtained from the proposed model outperform the classical estimates. According to the result, 40.92% of the children were born with low birth weight. The study also found that low birth weight is unevenly distributed across different regions of the country with the highest amounts of variation observed in the Afar, Somali and Southern Nations, Nationalities, and Peoples regions as represented by the latent trait parameter of the model. In contrast, the lowest low birth weight variation was recorded in the Addis Ababa, Dire Dawa, and Amhara regions. Furthermore, there were significant associations between birth weight and several factors, including the age of the mother, number of antenatal care visits, order of birth and the body mass index as indicated by the average posterior beta values of (β1= -0.269, CI=-0.320, -0.220), (β2= -0.235, CI=-0.268, -0.202), (β3= -0.120, CI=-0.162, -0.074) and (β5= -0.257, CI=-0.291, -0.225). The study showed that the low birth weight estimates obtained from the latent trait model outperform the classical estimates. The study also revealed that the prevalence of low birth weight varies between different regions of the country, indicating the need for targeted interventions in areas with a higher prevalence. To effectively reduce the prevalence of low birth weight and improve maternal and child health outcomes, it is important to concentrate efforts on regions with a higher burden of low birth weight. This will help implement interventions that are tailored to the unique challenges and needs of each area. Health institutions should take measures to reduce low birth weight, with a special focus on the factors identified in this study.

Prediction of birth weight using body measurements of local caprine population kids raised in a low-input breeding mode under arid environment.

The aim of this study is to establish linear measurements of local goat kids at birth and their factors of variation, as well as their possible correlations with birth weight. Additionally, the study analyses statistical models and barymetric functions to predict birth weight of kids based on their morphometric data. The database includes data on 128 goat kids born to 89 goats and 9 bucks in the experimental goat herd at the El GORDHAB station of IRA. Average BW, withers height (L1), heart girth (L2), rump height (L3), and body length (L4) of all kids were 2.45kg, 32.66cm,30.56cm, 33.41cm, and 31.21cm respectively. Results show that in general, local goat kids are small in size and weight at birth, which varies depending on sex, dam age, and type of birth. Highest and positive correlation coefficient value between birth weight and heart girth were observed (r = 0.95). The coefficient of determination (R2) for heart girth (0.78) was higher than other body measurements in single trait evaluation indicating it as the best trait for the predication of birth weight. The most appropriate combination of body measurements (R2 = 0.82) was observed between height at withers and heart girth for predication of birth weight estimation. Developing a system for recording birth weight based on easily obtainable body measurements could be a useful approach for rural areas. Result join the study objective by conceiving feasible genetic improvement plans for agropastoral herds by establishing individual phenotypes estimation even when the classical animal management does not already allowed.