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Fetal Growth

Fetal Growth describes the normal developmental process of the fetus during pregnancy.
This includes the physical growth and maturation of the fetus, as well as the physiological and biochemical changes that occur throughout gestation.
Factors influencing fetal growth, such as maternal nutrition, placental function, and genetic influences, are important areas of study.
Understanding normal fetal growth patterns is crucial for identifying and managing potential growth-related issues or complications.
Researchers in this field utilize a variety of techniques, including imaging, biomarker analysis, and clinical assessments, to monitor fetal development and optimize outcomes for both the mother and the child.

Most cited protocols related to «Fetal Growth»

1
Preterm Growth Chart Revision
To revise the growth chart, thorough literature searches were performed to find published and unpublished population-based preterm size at birth (weight, length, and/or head circumference) references. The inclusion criteria, defined a priori, designed to minimize bias by restriction [13 ], were to locate population-based studies of preterm fetal growth, from developed countries with:
a) Corrected gestational ages through fetal ultrasound and/or infant assessment and/or statistical correction;
b) Data percentiles at 24 weeks gestational age or lower;
c) Sample of at least 25,000 babies, with more than 500 infants aged less than 30 weeks;
d) Separate data on females and males;
e) Data available numerically in published form or from authors,
f) Data collected within the past 25 years (1987 to 2012) to account for any secular trends.
Fenton T.R, & Kim J.H. (2013). A systematic review and meta-analysis to revise the Fenton growth chart for preterm infants. BMC Pediatrics, 13, 59.
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Publication 2013
Birth Weight Care, Prenatal Females Fetal Growth Fetal Ultrasonography Gestational Age Head Infant Males
2
Racial/Ethnic Differences in Fetal Growth

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Publication 2015
Birth Weight Cuboid Bone Ethnicity Females Fetal Growth Fetus Gestational Age Health Insurance Infant Males Pregnancy Tests Racial Groups Signs and Symptoms Student Ultrasonography Woman
3
Mild Gestational Diabetes Mellitus: Evaluation of Treatment
Women were invited to participate in this study if, between 24 weeks 0 days and 30 weeks 6 days of gestation, they had a blood glucose concentration between 135 and 200 mg per deciliter (between 7.5 and 11.1 mmol per liter) 1 hour after a 50-g glucose loading test. Women were excluded if they had preexisting diabetes, an abnormal result on a glucose screening test before 24 weeks of gestation, prior gestational diabetes, a history of stillbirth, multifetal gestation, asthma, or chronic hypertension; if they were taking corticosteroids; if there was a known fetal anomaly; or if imminent or preterm delivery was likely because of maternal disease or fetal conditions. All the women who pa rticipated in the study provided written informed consent. The study was approved by the human subjects committee at each participating center.
After an overnight fast, eligible women completed a blinded 3-hour 100-g oral glucose-tolerance test. Samples were analyzed at a central laboratory, and results were forwarded to the data coordinating center. Mild gestational diabetes mellitus was defined as a fasting glucose level of less than 95 mg per deciliter (5.3 mmol per liter) and two or three timed glucose measurements that exceeded established thresholds: 1-hour, 180 mg per deciliter (10.0 mmol per liter); 2-hour, 155 mg per deciliter (8.6 mmol per liter); and 3-hour, 140 mg per deciliter (7.8 mmol per liter).10 (link) Women who met these criteria were randomly assigned by the coordinating center, with the use of the simple urn method,11 (link) stratified by clinical center. The urn method minimizes the degree of imbalance in the number of patients assigned to each group by increasing the probability of a patient’s assignment to the group that has previously been selected least often. Women were assigned to receive either formal nutritional counseling and diet therapy,12 (link) along with insulin if required (treatment group) or usual prenatal care (control group). In addition, a cohort of women who had a positive result on the 50-g glucose loading test but a normal result on a subsequent oral glucose-tolerance test and who were matched with the study cohort according to race and body-mass index (the weight in kilograms divided by the square of the height in meters), dichotomized as less than 27 or 27 or more, were enrolled by the data coordinating center in the group that received usual prenatal care. By including this group of women who did not have gestational diabetes mellitus, the patients, their caregivers, and the study staff were unaware of whether women in the control group met the criteria for the diagnosis of mild gestational diabetes mellitus. Women with a fasting glucose level of 95 mg per deciliter or more on the diagnostic oral glucose-tolerance test were excluded from the study, and their condition was made known to their health care providers.
Ultrasonography was performed in all subjects before the oral glucose-tolerance test to confirm the gestational age. Women who were receiving treatment performed daily self-monitoring of their blood glucose (fasting and 2-hour postprandial measurements) with the use of a portable memory-based reflectance meter. Insulin was prescribed if the majority of fasting values or postprandial values between study visits were elevated (fasting glucose level, ≥95 mg per deciliter or 2-hour postprandial glucose level, ≥120 mg per deciliter [6.7 mmol per liter]). If, during a prenatal visit, there was a clinical suspicion of hyperglycemia in a patient who was in the control group, the blood glucose level could be measured at the discretion of the provider. If a random blood glucose level of 160 mg per deciliter (8.9 mmol per liter) or more or a fasting glucose level of 95 mg per deciliter or more was detected, the patient’s caregiver initiated treatment and notified the local principal investigator and study personnel.
Nonstress testing, biophysical profile testing, and ultrasonography to assess fetal growth were not performed routinely in the treatment group but were reserved for standard obstetrical indications. However, all the women who were enrolled in the study were instructed regarding the daily assessment of fetal activity.13 (link) If delivery was not the result of spontaneous labor, the rationale for the timing and method of delivery was documented.
Landon M.B., Spong C.Y., Thom E., Carpenter M.W., Ramin S.M., Casey B., Wapner R.J., Varner M.W., Rouse D.J., Thorp JM J.r., Sciscione A., Catalano P., Harper M., Saade G., Lain K.Y., Sorokin Y., Peaceman A.M., Tolosa J.E, & Anderson G.B. (2009). A Multicenter, Randomized Trial of Treatment for Mild Gestational Diabetes. The New England journal of medicine, 361(14), 1339-1348.
Publication 2009
Adrenal Cortex Hormones Asthma Blood Glucose Blood Glucose Self-Monitoring Care, Prenatal Diabetes Mellitus Diagnosis Fetal Anomalies Fetal Diseases Fetal Growth Fetal Movement Gestational Age Gestational Diabetes Glucose High Blood Pressures Homo sapiens Hyperglycemia Index, Body Mass Insulin Memory Mothers Obstetric Delivery Obstetric Labor Oral Glucose Tolerance Test Patients Pregnancy Premature Birth Therapy, Diet Ultrasonography Woman
4
INTERGROWTH-21st Fetal Growth Study
INTERGROWTH-21st is a multicenter, multiethnic, population-based project, conducted between 2009 and 2014 in eight urban areas in eight different countries: the cities of Pelotas, Brazil; Turin, Italy; Muscat, Oman; Oxford, UK; Seattle, USA; Shunyi County, Beijing, China; the central area of the city of Nagpur (Central Nagpur), Maharashtra, India; and the Parklands suburb of Nairobi, Kenya13 (link). Its primary aim was to study growth, health, nutrition and neurodevelopment of fetuses from < 14 + 0 weeks' gestation to 2 years of age, using the same conceptual framework as the World Health Organization (WHO) Multicentre Growth Reference Study12 (link), in order to produce prescriptive growth standards to complement the existing WHO Child Growth Standards14 .
These urban areas had to be located at low altitude (≤ 1600 m) and women receiving antenatal care had to plan to deliver in these institutions or in a similar hospital located in the same geographical area, and there had to be an absence or low levels of major, known, non-microbiological contamination such as pollution, domestic smoke, radiation or any other toxic substances, evaluated during the study period at the cluster level using a data collection form specifically developed for the project15 (link). In the eight urban areas, we selected all institutions providing pregnancy and intrapartum care, in which > 80% of deliveries occurred.
To generate the CRL data for our stated aims, women with a singleton pregnancy that was conceived naturally were asked to participate in the Fetal Growth Longitudinal Study (FGLS), one of the three main components of the INTERGROWTH-21st Project, whose study methods have been described in detail elsewhere13 (link). Briefly, we recruited women from the selected populations with no clinically relevant obstetric or gynecological history, who met the entry criteria of optimal health, nutrition, education and socioeconomic status to create a group of affluent, clinically healthy women who were at low risk of intrauterine growth restriction and preterm birth. Recruitment occurred prospectively and consecutively at 9 + 0 to 13 + 6 weeks' gestation as estimated by LMP provided that: (1) the date was certain; (2) the agreement between LMP and CRL dating was ≤ 7 days; (3) the women had a regular 24–32-day menstrual cycle; and (4) they had not been using hormonal contraception or breastfeeding in the preceding 2 months. The women, who were all well-educated and living in urban areas, reported the date and certainty of their LMP at their first antenatal clinic visit in response to specific questions.
A single type of ultrasound machine (Philips HD-9; Philips Ultrasound, Bothell, WA, USA) with an abdominal probe was the machine of choice to measure CRL. However, as the first contact with the study often occurred at several different clinics in the geographical area, it was considered acceptable to use other, locally available, machines for the CRL measurement at the first antenatal visit only, provided that they were evaluated and approved by the study team. All 39 ultrasonographers at the eight study sites underwent rigorous training and standardization specifically for CRL measurement16 (link). In accordance with the study's quality-control protocol, they also submitted images of the CRL measurements, which were reviewed blindly by our collaborators at the Société Française pour l'Amélioration des Pratiques Echographiques. The ultrasonographers were only certified to measure CRL in the study if they demonstrated adequate knowledge of the study protocol and the quality of the images submitted for review was satisfactory17 (link).
CRL was measured once using strict techniques and imaging criteria18 (link). A discrepancy between GA based on LMP and that derived from CRL of more than 7 days was a reason to exclude the woman from the study. All women were then followed to delivery with standardized antenatal care evaluation and regular ultrasound scans every 5 ± 1 weeks.
The INTERGROWTH-21st Project was approved by the Oxfordshire Research Ethics Committee ‘C’ (ref: 08/H0606/139) and the research ethics committees of the individual participating institutions, as well as the corresponding regional health authorities in which the project was implemented.
Papageorghiou A.T., Kennedy S.H., Salomon L.J., Ohuma E.O., Cheikh Ismail L., Barros F.C., Lambert A., Carvalho M., Jaffer Y.A., Bertino E., Gravett M.G., Altman D.G., Purwar M., Noble J.A., Pang R., Victora C.G., Bhutta Z.A, & Villar J. (2014). International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown–rump length in the first trimester of pregnancy. Ultrasound in Obstetrics & Gynecology, 44(6), 641-648.
Publication 2014
Abdomen Care, Prenatal Child Clinic Visits Ethics Committees, Research Fetal Growth Fetal Growth Retardation Fetus Hormonal Contraception Menstrual Cycle Muscle Rigidity Obstetric Delivery Poisons Pregnancy Premature Birth Radiation Smoke Ultrasonography Woman
5
Preconception BMI and Gestational Weight Gain
Participants were part of a longitudinal cohort, the Glowing Study (clinicaltrials.gov # NCT01131117), which is examining the effects of maternal body composition on infant birth weight, growth, body composition, and risk of overweight at 2 years old. Participants were recruited from 2011 to 2014 in a small southern city. Women were eligible if they had a single previous pregnancy, had a BMI between 18.5 and 35 kg/m2, and were 21 years of age or older. Exclusion criteria included having preexisting medical conditions (e.g., diabetes mellitus, hypertension), taking medications known to influence fetal growth (e.g., glucocorticoids, insulin, thyroid hormones), and planning to smoke or drink alcohol during the pregnancy. A total of 287 participants met the inclusion/exclusion criteria and were enrolled in the study. Participants were enrolled in the primary study if they were planning a pregnancy or were less than 10 weeks gestation. Of those women, 51 women completed a preconception visit and of those, 43 women had measured weight at 4–10 weeks and 12 weeks; we will focus on this subsample of 43 participants for these secondary analyses.
At the preconception visit, participants were advised to remain weight stable during the first trimester, consistent with the IOM guidelines [2 ]. All participants received information on the IOM’s GWG guidelines [2 ] tailored to their BMI category at the 4–10 weeks gestation visit as well as the rationale for GWG guidelines during pregnancy (i.e., maternal and child health). Research staff also introduced and explained a GWG graph (tailored to BMI category) that would be used to track the participant’s GWG throughout her pregnancy. During pregnancy, all participants received six behavioral intervention sessions (i.e., at 4–10, 12, 18, 24, 30, and 36 weeks gestation) designed to promote healthy GWG, with intensified intervention offered in the presence of excessive GWG. The intervention has been described in detail elsewhere [7 ].
Weight was measured in a hospital gown with no shoes to the nearest 0.1 kg using a calibrated tarred standing digital scale at all study visits under fasted conditions. Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer at preconception only. All measures were obtained in duplicate, with a third assessment if there is discrepancy between the first two. BMI was calculated from these measures [weight(kg)/height(m)2], and women were classified as normal weight (n = 22) or overweight/obese (n = 21) at the preconception visit [8 ]. Of those participants in the overweight/obese category at the preconception visit, 17 participants were overweight and 4 were obese. Informed consent was obtained from participants, and all study procedures were approved by the Institutional Review Board of the University of Arkansas for Medical Sciences.
Descriptive statistics were calculated to describe weight change over each interval, time interval to conception, and the proportion of women correctly classified using the first trimester weights. Cohen’s kappa (κ) statistic [9 (link)] was used to assess the agreement between preconception BMI classification (i.e., normal, obese, overweight) and BMI classification at 4–10 weeks and 12 weeks of pregnancy. Bland-Altman plots [10 (link)] were used to examine the agreement between preconception BMI and the later BMIs. Both mean bias and 95% limits of agreement were computed. Because a non-significant linear trend between the difference of paired BMI values and their average was observed, Bland-Altman’s limits of agreement were not adjusted for trend. Additionally, agreement between preconception BMI and the later BMI measurements was evaluated by computing and testing Lin’s concordance correlation coefficient [11 (link)]. Lin’s concordance correlation coefficient (CCC) provides an estimate of the degree to which repeated measurements deviate from the 45° line of perfect concordance. The concordance correlation coefficient combines measures of both precision and accuracy. Weight change from preconception was compared to first trimester weights by BMI category and based on whether BMI category changed using Wilcoxon’s rank-sum (Mann–Whitney) tests. Statistical analysis was performed using Stata 14.0 statistical package (Stata Corporation, College Station, TX, USA).
Krukowski R.A., West D.S., DiCarlo M., Shankar K., Cleves M.A., Saylors M.E, & Andres A. (2016). Are early first trimester weights valid proxies for preconception weight?. BMC Pregnancy and Childbirth, 16, 357.
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Publication 2016
Behavior Therapy Birth Weight Body Composition Children's Health Conception Diabetes Mellitus Ethics Committees, Research Fetal Growth Fingers Glucocorticoids High Blood Pressures Insulin Maternal Inheritance Mothers Obesity Pharmaceutical Preparations Pregnancy Smoke Thyroid Hormones Woman

Most recents protocols related to «Fetal Growth»

1
Fetal Brain Development Patterns in IVM

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Publication 2023
Brain Cerebellum Cerebral Hemispheres Cortex, Cerebral Fetal Development Fetal Growth Fetus Gestational Age Heart Ventricle Seizures Ventricles, Fourth Vermis, Cerebellar
2
Automated Gestational Age Estimation in High-Risk Pregnancies
The INTERBIO-21st Fetal Study was conducted, between 2012 and 2019, at six sites in Pelotas (Brazil), Nairobi (Kenya), Karachi (Pakistan), Soweto (South Africa), Mae Sot (Thailand) and Oxford (UK), all sites were urban, except Mae Sot which was a rural site29 (link). All aspects of the study, including the ultrasound protocol, were identical to FGLS except that GA was estimated by CRL measurement at <14 weeks’ gestation (as a certain LMP would not be expected in a large proportion of this high-risk cohort owing to maternal conditions, poor nutrition, anaemia etc); thus, any woman with a singleton pregnancy was eligible. Hence, the population was more heterogenous and at higher risk of fetal growth impairment because of exposures such as HIV, malaria, and malnutrition, adding external validity to the automated GA estimation model.
FGLS provided 293,811 images from 4233 pregnancies which were randomly split in the following manner on a per fetus basis: 75% for model training (219,974 images), 15% for validation (44,173 images) and 10% for testing (29,664 images). The validity of the GA estimation model was then tested on 94,832 images from 2443 pregnancies in the INTERBIO-21st Fetal Study. Due to the longitudinal nature of both studies, the data were selected with evenly distributed GAs across the second and third trimesters.
In a sub-analysis, we further validated the automated GA estimation model in SGA and LGA pregnancies, as these pose a particular challenge in GA estimation using methods based on biometry. These were identified by birth weight for GA and sex below the 10th or above the 90th centiles, respectively30 (link). Further analysis comparing MultiPlane to biometry-based estimates (Hadlock16 (link) and INTERGROWTH-21st7 (link),) was also performed for all pregnancies in the INTERGROWTH-21st test set and INTERBIO-21st. Finally, we performed subanalysis of MultiPlane’s performance across the participating sites in the INTERBIO-21st dataset.
Lee L.H., Bradburn E., Craik R., Yaqub M., Norris S.A., Ismail L.C., Ohuma E.O., Barros F.C., Lambert A., Carvalho M., Jaffer Y.A., Gravett M., Purwar M., Wu Q., Bertino E., Munim S., Min A.M., Bhutta Z., Villar J., Kennedy S.H., Noble J.A, & Papageorghiou A.T. (2023). Machine learning for accurate estimation of fetal gestational age based on ultrasound images. NPJ Digital Medicine, 6, 36.
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Publication 2023
Anemia Birth Weight Care, Prenatal Fetal Growth Fetus Genetic Heterogeneity Malaria Malnutrition Mothers Pregnancy Ultrasonography
3
Validated ML Model for Fetal Ultrasound
We developed a machine learning model based on ultrasound images from two independent datasets. Ultrasound images from the Fetal Growth Longitudinal Study (FGLS) of the INTERGROWTH-21st21 (link), Project were used to train, validate and test (internally validate) the model. External validation was then performed on ultrasound images from the INTERBIO-21st Fetal Study22 (link) which were unseen by the model during the development phase. During internal validation (using INTERGROWTH-21st data) and external validation (using INTERBIO-21st data) the model was blinded to the ground truth.
Lee L.H., Bradburn E., Craik R., Yaqub M., Norris S.A., Ismail L.C., Ohuma E.O., Barros F.C., Lambert A., Carvalho M., Jaffer Y.A., Gravett M., Purwar M., Wu Q., Bertino E., Munim S., Min A.M., Bhutta Z., Villar J., Kennedy S.H., Noble J.A, & Papageorghiou A.T. (2023). Machine learning for accurate estimation of fetal gestational age based on ultrasound images. NPJ Digital Medicine, 6, 36.
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Publication 2023
Care, Prenatal Fetal Growth Fetal Ultrasonography Ultrasonics
4
Evaluating Real-World Sex Differences in Atypical Head Size
We did not exclude pregnant women with comorbidities or demographic factors that can be associated with having fetuses at either of the growth extremes. It was important to include these women to evaluate the real-world sex differences in atypical head size. Additionally, we did not have sufficient data to identify all of these conditions or factors. Specifically, we did not perform a sensitivity analysis excluding women with obesity from our analysis because pre-pregnancy body mass index (BMI) was often not available in the EMR; therefore, we could not reliably differentiate women with obesity from women without obesity.
We did not include postnatal outcomes in this analysis, as we could not accurately identify these outcomes. Many pathologic causes of atypical head size are not identified during the birth hospitalization (18 (link), 19 (link)), an issue that may be more prominent among those with a HC at the edge of the typical range that were the focus of our study. We studied patients at a tertiary referral center with a wide catchment area and would have missed a substantial proportion of infants who had diagnoses made after discharge. Prenatal concerns can also influence postnatal care. Therefore, increased frequency of prenatal microcephaly in females and prenatal macrocephaly in males could bias the proportion of infants identified with pathologic causes of atypical head size. There are no accepted lists of conditions that cause atypical head size or guidelines regarding identification of such conditions. Finally, preliminary work in our institution revealed significant differences among clinicians regarding the likelihood that certain conditions caused atypical head size starting in the second trimester.
Brawley A.M., Schaefer E.W., Lucarelli E., Ural S.H., Chuang C.H., Hwang W., Paul I.M, & Daymont C. (2023). Differing prevalence of microcephaly and macrocephaly in male and female fetuses. Frontiers in Global Women's Health, 4, 1080175.
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Publication 2023
Childbirth Diagnosis Females Fetal Growth Head Hospitalization Hypersensitivity Index, Body Mass Infant Macrocephaly Males Microcephaly Obesity Patient Discharge Patients Postnatal Care Pregnancy Pregnant Women Woman
5
Cohort Study of HIV+ Pregnant Women
This retrospective cohort study comprised singleton WLH pregnancies followed up at our obstetrics department (Hospital das Clínicas, São Paulo University Medical School, São Paulo – Brazil) between 2006 and 2019.
A database search was conducted to identify all cases of WLH evaluated in our department during the study period. Cases of multiple pregnancies, loss to follow-up, delivery at other hospitals, absence of prenatal care, unawareness of HIV diagnosis before delivery, and unavailability of hospital charts were not included.
Patient charts were revised, and data regarding maternal demographic characteristics, HIV infection, ART exposure, and obstetric and neonatal outcomes were assessed. HIV-related aspects considered were the type of transmission (perinatally vs. behaviorally), time of diagnosis, Viral Load (VL), CD4+ cell count, signs of immunodeficiency (CD4+ cell count < 200 mm3 or opportunistic infections), and genotype testing. Laboratory analyses were considered at baseline (first appointment) and 34 weeks of gestation.
Patients were considered to have PHIV if there was clear information on patients’ charts about such a type of transmission, based on HIV-positive mothers and diagnosis during childhood. Other cases were classified as Behaviorally acquired HIV (BHIV) cases.
The perinatal outcomes assessed included gestational age at delivery, preterm birth, low birth weight (< 2500 g), gestational diabetes, preeclampsia, fetal growth restriction, preterm labor, abnormal fetal well-being, and HIV Mother-To-Child Transmission (MTCT).
Throughout the study period, there were different guidelines for caring for pregnant WLH. Follow-up of this group of patients comprised regular prenatal appointments, first and second-trimester morphology scans, and regular assessment of fetal growth and wellbeing. All patients were prescribed ART, which consisted of three active antiretroviral drugs. The patients were assisted by a multidisciplinary team consisting of obstetricians, infectologists, psychologists, and social workers.
Historically, local practices regarding pregnant WLH tended toward elective cesarean delivery, irrespective of VL. Such policies have changed, and vaginal births have recently been encouraged. During the study period, zidovudine intrapartum prophylaxis and neonatal zidovudine syrup were routinely recommended to all the patients. Breastfeeding was contraindicated.
Indications for genotype testing during the study period included virological failure and, most recently, ART-naïve pregnant women. Data on genotype testing were assessed retrospectively, based on available test results on patients’ charts and the “Brazilian National Network of CD4+/CD8+ Lymphocyte Count and Viral Load” (SISCEL).
Osmundo GD J.u.n.i.o.r., da Costa R.A., Ruocco R.M, & Francisco R.P. (2023). Pregnancy in women living with perinatally acquired HIV: Perinatal outcomes and drug resistance profile. Clinics, 78, 100174.
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Publication 2023
Care, Prenatal CD4+ Cell Counts Cesarean Section Diagnosis Fetal Growth Fetal Growth Retardation Genotype Gestational Age Gestational Diabetes HIV Infections Immunologic Deficiency Syndromes Infant, Newborn Maternal-Fetal Infection Transmission Mothers Obstetric Delivery Obstetrician Opportunistic Infections Patients Pharmaceutical Preparations Pre-Eclampsia Pregnancy Pregnant Women Premature Birth Premature Obstetric Labor Radionuclide Imaging Transmission, Communicable Disease Vagina Zidovudine

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More about "Fetal Growth"

Fetal development, intrauterine growth, prenatal growth, gestation, pregnancy, maternal-fetal health, placenta, umbilical cord, ultrasound, biomarkers, fetal biometry, gestational age, fetal weight, fetal length, fetal head circumference, DMEM, non-essential amino acids, SAS 9.4, Stata 15, GraphPad Prism v6, HiSeq 2000.
The process of fetal growth and development is a crucial area of study in reproductive and maternal-child health.
This complex process involves the physical, physiological, and biochemical changes that occur within the fetus during the course of pregnancy.
Factors such as maternal nutrition, placental function, and genetic influences can all impact fetal growth patterns.
Researchers utilize a variety of techniques to monitor and assess fetal growth, including imaging modalities like the Voluson E8 ultrasound machine, as well as biomarker analysis and clinical assessments.
Advanced statistical software like SAS version 9.4, Stata 15, and GraphPad Prism v6 are often employed to analyze the data and identify trends or anomalies.
Understanding normal fetal growth trajectories is essential for recognizing and managing potential issues or complications, such as intrauterine growth restriction or macrosomia.
By leveraging the power of AI-driven platforms like PubCompare.ai, researchers can quickly locate the best protocols and techniques from scholarly literature, pre-prints, and patents to optimize their fetal growth studies and drive progress in this critical field of inquiry.