Colostrum quality was numerically better in the herd that appeared also better managed in comparison to the other study herd. Colostrum intake tended to be significantly associated with the total lactobacilli count in the better-managed herd. In conclusion, herd-level factors clearly contribute to the microbiota of pigs, but birth weight also plays a potential role in the gastrointestinal tract lactobacilli dynamics. Our results revealed a potential long-term effect of colostrum, and therefore give a reason to investigate more thoroughly the associations between maternal immunity, pig microbiota, and performance.Selection for hyper-prolific sows has led to increased litter size, decreased birth weight, and increased within-litter variation. This is accompanied by impaired colostrum intake of piglets and poor performance. We aimed to investigate the total count of fecal lactobacilli and species diversity in growing pigs on two herds. Study pigs were categorized either small or large according to their birth weight. Sow colostrum quality and colostrum supply of piglets were determined. We hypothesized that the birth weight and growth performance of pigs are associated with fecal lactobacilli composition, which is influenced by colostrum. Small pigs had higher lactobacilli counts in both herds, but the difference was significant only for one herd (n = 12) or large  at birth were followed from birth to slaughter in two commercial herds, H1 and H2. Herds differed in terms of their general management. We determined sow colostrum quality, colostrum intake, piglet serum immunoglobulins, and pig growth. We took individual fecal samples from pigs in the weaning and finishing units. We studied lactobacilli count and identified their diversity with 16S PCR. Total lactobacilli count increased in H1 and decreased in H2 between samplings. Lactobacilli species diversity was higher in H1 in both fecal sampling points, whereas diversity decreased over time in both herds. We identified altogether seven lactobacilli species with a maximum of five (one to five) species in one herd. However, a relatively large proportion of lactobacilli remained unidentified with the used sequencing technique. Small pigs had higher lactobacilli counts in both herds but the difference was significant only in H2 (p = 0.01). Colostrum quality was numerically better in H1 than in H2, where colostrum intake tended to be associated with total lactobacilli count (p = 0.05).We studied the fecal lactobacilli count and species diversity of growing pigs along with immune parameters associated with intestinal lactobacilli. Thirty pigs categorized as small (S,  The first microbial colonizers of a piglet gastrointestinal tract (GIT) originate from the sow birth canal during parturition and from the pen environment ,2. The sThe selection for hyper-prolific sow traits has led to an increase in litter size, and the average birth weight of piglets has consequently decreased , within-Antimicrobials are widely used in pig production, especially in young piglets . The incLactobacillus species are shown to have probiotic properties [Lactobacillus probiotics have the notification “generally regarded as safe” (GRAS) issued by the United States Food and Drug Administration (FDA) [Lactobacillus species of piglets born as either small (S) or large (L) [Lactobacilli are a thoroughly researched genus of Gram-positive bacteria. Several operties ,26. Furton (FDA) ,26 and aon (FDA) . Lactobaarge (L) ,28.The objective of our study was to investigate the total lactobacilli count and lactobacilli species diversity in growing pigs categorized either as S or L according to their body weight at birth. To investigate factors associated with the fecal lactobacilli contents, we determined sow colostrum quality and measured piglet serum immunoglobulins as an indicator for colostrum supply, and calculated pig growth. We hypothesized that the birth weight and growth performance of pigs are associated with GIT lactobacilli composition, which is influenced by colostrum quality. Additionally, the results of this pilot study will provide information for identifying potential probiotic lactobacilli in pig feces for further screening.The experiment was approved by the southern Finland Regional State Administrative Agency (ESAVI/16950/2018).Two commercial herds, hereby abbreviated H1 and H2, located in western and south-western Finland, participated voluntarily in the study. Herds H1 and H2 averaged 450 and 1100 sows, respectively. Sows in both herds were housed in standard farrowing pens with crates and partly slatted floors. Both herds had separate finishing units to which the pigs were transported at the approximate age of ten weeks . The disn = 12 from H1, n = 17 from H2) and piglets weighing ≥ 1.3 kg as L .One farrowing group per herd was included in the study. Eleven sows (DanBred × Landrace) gave birth to 126 piglets within four days in H1 and 254 piglets were born from 18 sows (Topigs Norsvin) within three days in H2. Piglets were weighed at birth and ear-tagged individually within the first 24 h post-partum. After this, they were followed until slaughter. Piglets weighing less than 0.9 kg at birth were excluded from the study due to their poor prediction of survival. Piglets weighing 0.9–1.2 kg at birth were categorized as S  was used to clean two teats and the surrounding skin of the udder. One researcher milked the sample using disposable gloves while another person kept the already born piglets away from the teats for the duration of sampling. The colostrum sample was placed in the refrigerator until immunoglobulin G (IgG) measurement with a Brix refractometer —a measurement reflecting the colostrum quality in this study. A drop of colostrum was placed on the daylight plate of the refractometer, and the result was read from the scale by viewing through the eyepiece. Reference values for colostrum quality were obtained from Hasan et al. . Colostrg for ten minutes. Serum immunoglobulins were measured the following day after sampling using an immunocrit method described by Vallet et al. [At the age of one to five days, one blood sample was taken from the jugular vein of each study pig. Samples were stored in the refrigerator overnight and centrifuged at 100× t et al. .Study piglets were checked for cross-fostering by comparing the information collected about the dam at birth to a nursing dam at the moment of blood sampling. If the piglet was suckling another sow at the time of blood sampling, it was marked as being cross-fostered. The pigs were weaned approximately at the age of four weeks into separate weaning departments in the piglet-producing herds. They were transported to finishing units, one for each piglet-producing unit, when they were about nine weeks old. The study pigs were weighed five times during the follow-up period. The average daily weight gain (ADG) of the pigs was calculated for the corresponding production stages. The follow-up period for the study pigs is summarized in Fecal samples were taken from the pigs in the weaning unit and again in the finishing unit . The recAfter all pigs were slaughtered, fecal samples from 30 pigs of 19 sows (8 in H1 and 11 in H2) were selected for lactobacilli analysis. The selection criteria included the following: 1. the dam of the pig had not received any medications during the gestation period before the birth of the study pig, 2. both fecal samples were available from the selected individuals, and 3. both S and L pigs were represented equally in the final sample. One to four piglets per sow fulfilled the selection criteria from H1 and one to two piglets from H2. The allocation procedure resulted in six S and eight L pigs from H1 and 6 S and 10 L pigs from H2. Finally, the selected frozen samples  were transported to the collaborative laboratory in a cool box within three hours, after which they were stored at −80 °C until lactobacilli analysis.−5, 10−6, and 10−7 were plated on BL agar  and incubated at 37 °C for 72 h, anaerobically . Colonies were counted and all different looking colonies were picked into Gifu Anaerobic Medium Broth  followed by incubation at 37 °C for 24 h. The grown colonies were viewed under a microscope and the non-motile rod-shaped bacteria were streaked on BL agar plates. The plates were incubated anaerobically at 37 °C for 72 h. For DNA isolation, one colony from each plate was suspended into 50 µL TE buffer, heated at 95 °C for 10 min, chilled on ice for 2 min, and centrifuged for 15 min at 17,000× g. The supernatant containing the DNA was stored at −20 °C.Fecal samples were thawed on ice overnight. Tenfold dilution series were made using 1 g of feces in 0.85% saline. The serial dilutions 10w/v) agarose gel with gel electrophoresis and visualized with ethidium bromide under UV light.To identify different lactobacilli, a colony PCR was performed with lactobacilli strain-specific primers. The PCR was conducted using 1 µL of DNA, 12.5 µL of 2x My Taq HS Red Mix , 10.5 µL of RNAse free water, and 0.5 µL of 0.2 µM primers Lac1 (5′-AGCAGTAGGGAATCTTCCA-3′) and s-g-Lab-0677-a-A-17 (5′-CACCGCTACACATGGAG-3′) with a final volume of 25 µL. For the PCR conditions, the initial denaturation step was at 95 °C for 1 min; 35 cycles of denaturation at 95 °C for 15 s, primer annealing at 58 °C for 15 s, extension at 72 °C for 15 s, and final extension at 72 °C for 10 min . The PCR product was run on a 1% , and 1492R (5′-CGGTTACCTTGTTACGACTT-3′) was performed in the PCR conditions of initial denaturation at 95 °C for 1 min; 35 cycles of denaturation at 95 °C for 15 s, primer annealing at 53 °C for 15 s, extension at 72 °C for 15 s, and final extension at 72 °C for 10 min. The PCR product was purified with polyethylene glycol 8000/NaCl . The purified DNA was measured and diluted correctly for sequencing performed by the Institute for Molecular Medicine Finland . The lactobacilli species were identified using the BLAST database of the National Center for Biotechnology Information (NCBI) with 96% accuracy.n = 18) did not follow a normal distribution and thus a Mann-Whitney U test was used to test for differences between herds in colostrum quality.SPSS (IBM SPSS Statistics 25) was used in the statistical analyses. The normality of the continuous variables was assessed visually (histogram) and with the Shapiro-Wilk test. Parity (range 1–5) was re-categorized into three categories; 1: gilts, 2: 2nd to 3rd parity, and 3: 4th to 5th parity, respectively. Colostrum quality measures  as a covariate. Herd and birth size were included as fixed factors in the model. Interaction between herd and birth size was included in the preliminary model but discarded because it was not significant.p ≤ 0.25 in the univariate analyses were included in a mixed model with fecal sampling point as a repeated effect. In addition to the variables selected based on the results from the univariate analyses, the fecal sampling point was also included as a fixed factor, and all relevant interactions were tested. The following factors were significantly associated and were thus tested in separate models: average bodyweight (BW)  with ADG (during post-weaning and finishing periods); and CI with piglet size at birth (S/L). Non-significant factors (p ≥ 0.1) and interactions were excluded from the model one by one. As ADG and BW correlated, a separate model was run with BW, but BW was not significantly associated with the lactobacilli count. Herd, birth size, and their interaction were included in the final model as fixed factors, and ADG was included as a covariate.Due to skewness of the total lactobacilli count, it was log-transformed for the mixed-model procedure and subsequently back-transformed to present the results. Univariate analyses were used to test single associations between the explanatory variables of interest and total lactobacilli count .Variablep ≥ 0.1) were excluded one by one. The final model only included CI.Due to limited data, the effects of CI on the total lactobacilli count were tested only for H1 data. In the first step, the sampling point and birth size were included as fixed factors and ADG and CI as covariates. As birth size and CI were correlated, they were tested in separate models. Non-significant factors .Colostrum quality was numerically better in H1 compared to H2: the median colostrum quality in H1 was 27.5, ranging from 24 to 32, and 25.5 in H2, ranging from 21 to 26. In H1, one sample out of eight (12.5%) was “borderline”, four samples (50.0%) were “adequate”, and three samples (37.5%) were “very good”. In H2, three out of 11 samples (27.3%) were “borderline”, seven (63.6%) were “adequate”, and one sample was omitted due to problems while measuring the Brix value. The mean piglet serum immunoglobulin value was 0.13 (SD 0.03) for both herds. One high piglet serum immunoglobulin value (0.35) was discarded from H1 to normalize the data. As a result of the mixed model, the mean serum immunoglobulin values were numerically larger in H1 and for L piglets, but differences were not statistically significant. Herd, piglet birth size, or piglet age during blood sampling were not associated with the serum immunoglobulin results  had received an antimicrobial treatment course before the first fecal sampling point. After that, verifying the information concerning the treatments was not possible.Total lactobacilli count (CFU/mL) numerically increased in H1 and decreased in H2 over time. Lactobacilli counts in both herds and pig sizes at birth are shown in p = 0.02). Estimated marginal (EM) means for the interaction term herd*size showed that the difference in total lactobacilli count between size categories was seen in H2, where S piglets had a significantly higher lactobacilli count compared to L piglets  (p = 0.09).As a result of the mixed model, the birth size was associated with the total lactobacilli count  . Moreove = 0.01) . Averagep = 0.05).In the model including CI (only H1), CI tended to be significantly associated to the total lactobacilli count  in H1 and from zero to two (median 1) in H2, respectively. At fecal sampling point 2, the numbers of lactobacilli spp. ranged from zero to three (median one) in H1 and from zero to one (median one) in H2, respectively.Altogether seven lactobacilli were identified in the feces of the study piglets in decreasing order of prevalence: p = 0.01). All seven lactobacilli spp. were present in H1, whereas only two species were present in H2 , our results are in line with a previous finding where S pigs remain small in the long term and their average daily gains are lower compared to pigs born with normal bodyweights . InteresLactobacillus counts between 7 and 35 days of age than piglets with lighter birth weights (<1.1 kg), which in turn had a different gut microbial community and metabolic status compared to their normal-birth-weight siblings. The researchers [Lactobacillus composition is associated with the growth performance of piglets. ADG tended to be associated with the total lactobacilli count in our study pigs, but we were not able to model factors associated with the lactobacilli spp. diversity because of uncertainties regarding sequencing. The sequencing technique is one source of bias in our present study, as quite many lactobacilli remained unidentified.In a study by Li et al. , pigletsearchers  thereforearchers  also supL. reuteri and L. johnsonii. Lactobacilli are present throughout the GIT, but principally exhibit higher levels in fecal samples [Lactobacillus species. Moreover, Lactobacillus has been reported to be the most dominant genera in pig GITs from birth until 56 days of age [Lactobacillus spp. diversity in their ileal contents at the age of 35 days (35.7%) and in their colons between the age of 7 and 35 days (62.9%). According to Li et al. [Lactobacillus in the pre-term group and increased bacterial diversity in the full-term infant group. Pre-term infants had significantly more lactobacilli in their meconium compared to full-term infants [More lactobacilli spp. were identified in H1 at both fecal sampling points and similar lactobacilli spp. were the most abundant in both herds:  samples ,34. Furts of age . Piglet s of age  found S i et al. , birth wi et al. . Recentli et al.  showed t infants  have repKim et al.  showed tL. reuteri was the predominant lactobacilli sp. identified in this study in both herds and over time. L. reuteri is known to be acid-tolerant and resistant to swine bile and to treatment with chlortetracycline in vitro [L. reuteri resulted in a richer and unique jejunal microbiome when compared between control and antimicrobial treated pigs, and no significant effect of treatment was observed on the cecal and colonic microbiome [L. reuteri administered to piglets colonizes the intestinal mucosa and improves the cecal microbiota profile and whole-body antioxidant and immune status, leading to better growth and lower morbidity and mortality rates [Lactobacillus spp. with higher percentages were found in recovered piglets compared to diarrheal ones: L. reuteri, L. amylovorus, L. acidophilus, L. johnsonii, and L. crispatus [L. reuteri currently has promising potential as a probiotic candidate and strain-level investigations are warranted. Four out of five lactobacilli highlighted by Bin et al. [in vitro . Treatmecrobiome . Moreovecrobiome . Yang etty rates . The folrispatus . Supportn et al.  were alsCertain factors were out of the scope of this study, such as detailed investigation of feed composition and antimicrobial treatments of the study pigs, which could potentially have influenced the results. Further, weaning practices on farms and transportation to the finishing units may have influenced the results as well. Konstantinov et al.  found weBoth herd-level and individual differences in the fecal lactobacilli dynamics of growing pigs were identified in the present study. Regardless of the perceived poorer management practices in herd 1 as compared to herd 2, lactobacilli diversity was significantly higher in this herd at both sampling points. Small piglets had a numerically higher lactobacilli count throughout the study and this difference was significant in H2. In summary, herd-level factors clearly contribute to the microbiota of pigs but birth weight also plays a potential role in the GIT lactobacilli dynamics. Our present study strengthens the assumption of the importance of colostrum as a contributor to fecal lactobacilli composition of the progeny, and this influence seems to be long term. Therefore, the associations between maternal immunity, pig microbiota, and performance are encouraged to be investigated more thoroughly.


A reliable prediction of foetal birth weight aids in decision regarding time and mode of delivery.This study aimed to determine the accuracy of the product of symphysio-fundal height and abdominal girth in predicting birth weight among pregnant women in Keffi, Nigeria.The study involved pregnant women presenting for delivery at the Federal Medical Centre, Keffi, Nigeria from July to October 2017.p < 0.05 and 95% confidence level.One hundred and fifty-three pregnant women at term with singleton foetuses were recruited by systematic random sampling. An interviewer-administered questionnaire was used to obtain their socio-demographic data, past medical and obstetric history. Symphysio-fundal height and abdominal girth measurements were used to calculate the estimated foetal weight. This was compared with the actual birth weight. Absolute percentage error was used to determine the overall predictive error of Dare’s formula. Data were analysed using SPSS version 20.0. Statistical significance was set at r = 0.52, p < 0.001) between the estimated foetal weight and the actual birth weight. Ninety (66.2%) of the babies within normal weight and six (85.7%) of macrosomic babies were correctly predicted. None of the low birth weight babies was correctly predicted by the formula.The mean age of the participants was 29.65 ± 5.15 years. The mean gestational age was 39.5 ± 1.2 weeks. There was a significant correlation (Dare’s formula accurately predicted normal weight and macrosomic babies, but less accurately predicted low birth weight babies. It is an important determinant of perinatal outcomes. The determinants of birth weight include gestational age at delivery, maternal race and other parental, environmental and pregnancy-specific factors.2 The normal birth weight ranges between 2500 g and 4000 g. Birth weights outside this range have been found to be associated with increased risks of perinatal morbidity and mortality.4Birth weight is the first weight of the newborn obtained shortly after birth.5Prenatal estimation of foetal weight is an important aspect of birth preparedness. It aids in decision-making and preparations regarding the time and mode of delivery, perinatal care required and level of care or facility required. It is also useful for assessing prognosis and perinatal counselling on likelihood of survival of preterm and small for gestational age (SGA) babies.3Foetal weight estimation can be done by biochemical, clinical and radiological means. None of the currently available techniques of foetal weight estimation is foolproof in terms of accuracy.2 Clinical methods are less costly and can be performed by less experienced examiners. Ultrasonographic foetal weight estimation requires expensive equipment and trained personnel. The clinical method is a useful alternative in resource-poor settings.The two most commonly used methods for predicting birth weight are the clinical and ultrasonographic methods.2 The birth weight prediction formulas are based on symphysio-fundal height (SFH) measurements. These include Johnson’s, Ojwang’s, Dare’s and Dawns’ formulas amongst others. Some of these formulas are quite complex and difficult for routine use.2Clinical methods of foetal weight estimation include tactile assessment of foetal size, clinical risk factor estimation, maternal self-estimated foetal weight (EFW) and birth weight prediction equations.8 There is, however, paucity of studies on the accuracy of this formula in the authors’ region of practice. This study aimed to determine the accuracy of Dare’s formula in the prediction of birth weight amongst term pregnant women in the Federal Medical Centre (FMC), Keffi, Nigeria.Dare’s formula is a simple formula in which the product of abdominal girth (AG) and SFH is used to estimate foetal weight. It has been found to correlate well with actual birth weight (ABW) in previous studies.It was a cross-sectional analytical study conducted from 10 July to 31 October 2017 at the labour ward of the FMC, Keffi. Keffi is the Headquarters of Keffi Local Government Area of Nasarawa State, Nigeria and has an estimated population of 92 550 . An average of 200 new bookings and 500 return patients are seen monthly in the antenatal clinic, with an average of 120 deliveries per month.All pregnant women who presented to the labour ward for delivery during the study period were eligible to participate in the study. The inclusion criteria were as follows:Booked pregnant women at term .Women whose gestational ages could be confirmed by last menstrual date or ultrasound dating at or before 20 weeks gestation.Patients with live singleton foetus in longitudinal lie with intact membranes.Patients in the first stage of spontaneous labour and those admitted for elective induction of labour or caesarean delivery.Patients who granted consent to participate in the study.Comprehensive physical examination and ultrasound scan were conducted for all the women at the time of recruitment into the study. Those who had fibroid, ovarian tumour or cyst and poly or oligohydramnios were excluded from the study.9Z is the 1.96 which is the standard normal deviate at 95% confidence interval level and p is the expected proportion of estimated birth weight correct to within 10% of ABW. This study used 69.5% from a similar study conducted by Njoku et al.10 in Calabar, Nigeria; e is the absolute precision level of 5%.The minimum sample size was calculated using the Cochrane formula for determining the representative sample for proportions in a large population and the finite population correction for proportions recommended for adjusting the sample size for small study populations:Hence:9 was applied to get the appropriate sample size as follows:N is the study population size and n is the adjusted minimum sample size:Because the population under study was about 240 women expected to deliver during the study period, the formula for finite population correctionThus, the minimum sample size required for the study was 139. An additional 10% of this (14) was added to allow for possible data loss. Therefore, 153 participants were recruited for the study.The nature and purpose of the study were explained to prospective participants during group antenatal health education sessions. The participants were recruited through a systematic random sampling technique. An estimated 120 women were expected to deliver per month over the 8 weeks of the study, giving a sampling frame of 240 and a sampling interval of 2. The first participant was selected by random sampling through a ballot. Thereafter, every second eligible and consenting participant was selected until 153 women who fulfilled the inclusion criteria were recruited. Interviewer-administered questionnaires were used to obtain socio-demographic data and medical and obstetric history. Measurements of the SFH and AG were taken by the principal researcher or trained designated senior resident doctors using a flexible tape measure calibrated in centimetres. This was used to calculate the EFW in grams using the formula (EFW) in grams = SFH × AG in cm, where 1 cm = 1 g. The babies were weighed within 1 h of delivery by the principal researcher and trained midwives using an analogue neonatal weighing scale (Waymaster® England). The scale was frequently checked and adjusted for zero error.t-test. Accuracy of estimated birth weight was determined using percentage error calculated as [(ABW–EBW)/ABW] × 100. This provided information on whether Dare’s formula overestimated or underestimated the BW of babies. Also, absolute percentage error was used to determine the overall predictive error of Dare’s formula. Frequencies and percentages were used to determine accuracy within 10% of ABW. Statistical significance was set at p < 0.05 and 95% confidence interval level.Data were analysed using IBM SPSS Statistics for Windows, version 20.0. . The EFW was compared with the ABW using paired samples Ethical clearance for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre Keffi . Participation in the study was voluntary and all participants provided written, informed consent at the time of recruitment.The mean age of the participants was 29.65 ± 5.15 years. All the participants were married. About half of them  had secondary education. The majority of the participants  were employed. The mean estimated gestational age of the participants at delivery was 39.5 ± 1.2 weeks. The mean parity was 1.62 ± 1.50. Only a few of the women had co-morbidities that could have impacted negatively the birth weight. Fifteen (9.8%) participants had a history of high blood pressure, two (1.3%) had diabetes mellitus, three (2%) had anaemia, whilst nine (5.9%) and three (2%) had a history of delivery of large and small babies, respectively. The socio-demographic, obstetric and medical details of the participants are presented in The mean EFW was 3870 g ± 620 g. Majority of the babies, 136 (88.9%), were within the normal birth weight range of 2500 g – 4000 g. The mean ABW was 3190 g ± 460 g .Comparison of EFW and ABW showed that Dare’s formula underestimated the number of babies whose ABW was less than 3500 g, whilst it overestimated the babies whose ABW was 3500 g and above .r = 0.5, p ≤ 0.001) between the EFW and ABW. There was significant difference in the means of EFW and ABW.Only 37 (24.2%) of the study participants had their EFW correct to within 10% of the ABW. About 90 (66%) of the babies with birth weights within normal range and 6 (86%) of macrosomic babies were correctly predicted. None of the low birth weight babies was correctly predicted by the formula. The EFW using the Dare’s formula correlated best with the ABW within the 3500 g – 3999 g range. There was a positive and significant correlation  mean maternal age at childbirth which is 29.8 years.11 The mean parity of the participants in this study (1.6) was comparable to findings in other studies. Malik et al.12 in Islamabad had a mean parity of 1.66 ± 1.6 amongst their study participants. Torloni et al.6 in Brazil had a mean parity of 1.2 ± 1.5 amongst their study participants. The contrary was the case in Kano, North-Western Nigeria, where Ugwa et al.13 had a parity of 3 ± 2 amongst their study participants. This may be because of low level of education, early marriages and higher births amongst women in the North-Western region of Nigeria compared to Keffi in North-Central Nigeria.14 Female education has been found to increase the uptake of contraceptives with subsequent reduction in the rate of childbirth.15 The higher percentage of educated participants in this study might be responsible for the lower mean parity.In this study, Dare’s formula (product of SFH and AG) was used to predict birth weight amongst term pregnant women in FMC, Keffi, Nigeria. The mean age of the participants in this study was comparable to what was found in similar studies conducted elsewhere in Nigeria,16 in India. Their study involved 303 pregnant women of any parity and age with a period of gestation greater than 28 weeks and in any stage of labour. They found a stronger correlation (0.75) between EFW and ABW. The reason for this may be the exclusion of obese patients from their study. Thombarapu and Agrawal17 in their study in India also had a higher correlation (0.726) between EFW and ABW using the Dare’s formula. Ugwu et al.8 in Enugu, Nigeria, also had a stronger correlation (0.71) between the EFWs and ABWs amongst their study participants. This may also be a result of the exclusion of women that weighed greater than 95 kg from their study. Weight of the participants was not considered in this study in Keffi. This may explain the lower correlation between the EFWs and ABWs in this study.In this study, the correlation factor between EFW and the ABW was 0.52. This was lower than what was found in the study by Sharma et al.6 in a study conducted in Brazil compared the accuracy of Dare’s formula, Johnson’s formula and mother’s assessment of foetal weight and ultrasound in prediction of birth weight. They analysed the results obtained from 100 women with full-term, cephalic, singleton pregnancies who delivered within 3 days of the foetal weight estimation. They found that birth weights were correctly estimated to within 10% of the actual in 59%, 57%, 61% and 65% of the participants using the mother’s estimate, Dare’s formula, Johnson’s formula and ultrasound estimate, respectively. They postulated that Dare’s formula was less accurate than Johnson’s formula for prediction of birth weight because of the lack of correction for obesity in the Dare’s formula. Twenty-four per cent of their participants were obese. Thus, they recommended a larger study to test the accuracy of the Dare’s formula in obese women.6 Another study conducted by Deeluea et al.18 in Thailand showed differences in the fundal height growth curves of underweight, overweight and obese pregnant women compared to those with normal weights. It can be extrapolated from this study that the weight of the woman influences the SFH and by extension of the EFW because the SFH is one of the variables used to calculate the EFW in the Dare’s formula.Torloni et al.7 in Iran. They found a correlation of 0.56 between the EFW and the ABW amongst their study participants. It was also deduced from their study that the Dare’s formula will predict foetal macrosomia at a cut-off point of 3900 g and low birth weight at a cut-off point of 3000 g. This implied a relative underestimation of large birth weight babies and overestimation of the weight of low birth weight babies.7 This was contrary to the findings in the present study, where the formula was found to underestimate weights below 3500 g and overestimate birth weights from 3500 g and above. Mortazavi and Akaberi evaluated 795 participants in their study. This was a larger study compared to the present study. They also utilised a non-random sampling technique in recruiting their participants thus giving room for bias. They also excluded women who weighed greater than 91 kg which would have screened out women who are likely to have large birth weight babies.The correlation in this study was, however, comparable to what was found by Mortazavi and Akaberi16 found that there was no significant difference between the mean EFW obtained from the product of SFH and AG and the mean ABW in their study participants. The exclusion of obese women from their study might have given them better results. Their study participants were also not limited to those with term pregnancies. They included women that reported in labour from greater than or equal to 28 weeks gestation.Unlike the finding in this study, Sharma et al.19 in India. The study by Yadav et al. was carried out over a longer duration (18 months) than the present study and they had a larger number of participants (200). This might have been responsible for the differences in the results obtained. The racial differences might have also played a role.The proportion of the participants who had EFW correct to within 10% of ABW in this study was quite low (24.2%) compared to 81% found amongst the participants of the study by Yadav et al.20 in Iran also had a higher proportion of their participants (68%) with EFW within 10% of the ABW. They, however, had a smaller sample size with 98 participants. Torloni et al.,6 despite not excluding obese mothers from their study in Brazil, had a higher proportion of their participants (57%) with EFW (predicted from Dare’s formula) within 10% of ABWs. It was, however, observed that their sampling technique was biased as the women who reported to the study centre during the period of their study did not have an equal chance of being selected to participate in the study. They also had a smaller sample size (100) compared to the present study.Esmaeilou and Mohammadi8 in Enugu, Nigeria was comparable to this study. They had 35% of the EFWs for their participants within 10% of ABW. They compared the accuracy of ultrasonic method of foetal weight estimation and the clinical method using Dare’s formula. They found that both methods overestimated birth weight with the error more with the clinical method (Dare’s formula). It was noted that the participants of the present study and that of Ugwu et al. in Enugu had similar characteristics. The mean maternal age, parity and mean gestational age were similar in both studies. This probably accounted for the similarity of the findings of both studies.The percentage of the participants that had EFW correct to within 10% of the ABW in the study by Ugwu et al.21 in India found that Dare’s formula overestimated birth weight in 84% of the participants in their study. This was higher than was found in the present study, where Dare’s formula overestimated birth weight in 33.8% of babies with normal weight. Their study was, however, a non-randomized study which was undertaken over a longer duration of 14 months. Their study participants included those with gestational period greater than 34 weeks and obese women were excluded from their study. Hence, the disparity in the findings of the two studies.Malik et al.About 24% of the study participants had their EFW correct to within 10% of the ABW in this study. The EFW correlated well with the ABW with a correlation factor of 0.52. The findings of this study showed that the Dare’s formula was fairly accurate in the prediction of normal weight (66.2%) and macrosomic babies (85.7%). The formula failed to accurately predict low birth weight babies. This suggests that its use in this environment may be limited in case of suspected low birth weight. Dare’s formula should be combined with other methods of foetal weight estimation to make decisions on the management of such patients.Further research to compare the accuracy of birth weight prediction with Dare’s formula in obese and non-obese mothers is recommended.


Maternal prenatal stress exposure (PNSE) increases risk for adverse psychiatric and behavioral outcomes in offspring. The biological basis for this elevated risk is poorly understood but may involve alterations to the neurodevelopmental trajectory of white matter tracts within the limbic system, particularly the uncinate fasciculus. Additionally, preterm birth is associated with both impaired white matter development and adverse developmental outcomes. In this study we hypothesized that higher maternal PNSE was associated with altered uncinate fasciculus microstructure in offspring.In this study, 251 preterm infants   underwent brain magnetic resonance imaging including diffusion-weighted imaging around term-equivalent age . Measures of white matter microstructure were calculated for the uncinate fasciculus and the inferior longitudinal fasciculus, a control tract that we hypothesized was not associated with maternal PNSE. Multiple regressions were used to investigate the relationship among maternal trait anxiety scores, stressful life events, and white matter microstructure indices in the neonatal brain.q = .007), radial diffusivity , and mean diffusivity  in the left uncinate fasciculus, and higher axial diffusivity  in the right uncinate fasciculus.Adjusting for gestational age at birth, postmenstrual age at scan, maternal age, socioeconomic status, sex, and number of days on parenteral nutrition, higher stressful life events scores were associated with higher axial diffusivity (β = .177, These findings suggest that PNSE is associated with altered development of specific frontolimbic pathways in preterm neonates as early as term-equivalent age. VIDEO CONTENT ONLINESEE COMMENTARY ON PAGE 487; SEE ALSO Maternal prenatal stress exposure (PNSE) represents a global public health problem White matter development can be assessed in vivo using diffusion tensor imaging (DTI) PNSE has been linked to abnormal neurodevelopment of a number of brain regions including the limbic system and prefrontal cortex, in both animal Preterm birth affects approximately 11% of global live births and is associated with adverse neuropsychiatric and developmental outcomes In this study, we assessed the relationship between maternal PNSE and white matter microstructure of the uncinate fasciculus in a large sample of premature neonates. We hypothesized that higher scores on maternal stressful life events and trait anxiety would be associated with decreased FA and increased RD, AD, and MD in the uncinate fasciculus.n = 6) and cases with major focal lesions such as periventricular leukomalacia, hemorrhagic parenchymal infarction, and other ischemic or hemorrhagic lesions (n = 40) were excluded from analysis  took part in the Evaluation of Preterm Imaging Study (ePRIME), a randomized control trial that investigated the effect of having a brain magnetic resonance (MR) imaging or ultrasound scan at term-equivalent age on parental anxiety analysis . In the Ethical approval was obtained from the Hammersmith and Queen Charlotte’s Research Ethics Committee (09/H0707/98).n = 32) who had missing values on a maximum of 10% of questions . The questionnaire was adapted from the Avon Longitudinal Study of Parents and Children There were no missing data on the stressful life events questionnaire.b value of 750 s/mm2, 1 non-diffusion-weighted image, b = 0) were acquired on a Philips 3T  MR system sited on the neonatal intensive care unit using an 8-channel phased array head coil.Three-dimensional magnetization prepared rapid acquisition gradient echo , T2-weighted turbo spin echo , and single shot echo planar DTI  prior to scanning (219 infants were sedated). Pulse oximetry, temperature, and electrocardiography were monitored throughout the scan and ear protection was used, comprising earplugs molded from a silicone-based putty  placed in the external auditory meatus and neonatal earmuffs .http://.dti-tk.sourceforge.net) http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BETDiffusion-weighted images were visually inspected in 3 orthogonal planes for the presence of motion artifact, and corrupt diffusion-weighted volumes were excluded before tensor fitting. Seventy-seven datasets had at least 1 volume removed . Image processing and data analysis were performed using FMRIB's Diffusion Toolbox (version 3.0) and DTI-TK , graphs were created with R package ggplot2  r = .76, p < .001) and would have introduced multicollinearity in the regression analysis. The number of days on ventilation was not included as a covariate, as it was highly correlated with TPN , and both measures provide information on the health status of infants. There was no significant difference between male and female infants on any of the variables included in the model.The relationship between potential covariates and variables of interest was assessed through bivariate Pearson’s correlations . We asseA total of 251 infants  born prematurely were scanned at term-equivalent age. Demographic data are presented in n = 36), 1 event (n = 90), 2 events (n = 66), 3 events (n = 33), 4 events (n = 16), 5 events (n = 5), 6 events (n = 4), and 7 events (n = 1). The stressful life event scores were calculated for each participant based on the severity of experienced events . Using Spearman’s correlation, scores on the stressful life events measure did not correlate with trait anxiety .The number of stressful life events experienced by mothers ranged between 0 and 7 (median = 1 [interquartile range = 1–2]). This included mothers who had experienced no events , RD , and MD , as well as right uncinate fasciculus AD . q < .001). Partial regression scatterplots for nonsignificant relationships are reported in After controlling for GA, PMA, SES, TPN, sex, and maternal age, and after correcting for multiple comparisons, maternal stressful life events were associated with infant left uncinate fasciculus AD . The pattern of results remained the same as when these items were included.To check the robustness of the results, we repeated our analyses accounting for 1) imputed data for STAI-TR, 2) outliers, 3) postnatal age, 4) ethnicity, 5) multiple births, 6) days on ventilation, 7) emergency cesarean section, 8) intrauterine growth restriction, 9) pregnancy-induced hypertension, 10) larger sample, and 11) age range. The relationship between stressful life events and uncinate fasciculus microstructure retained significance see the .Preterm birth is associated with a range of adverse psychiatric and neurodevelopmental outcomes. To our knowledge, this is the first study examining the relationship between maternal PNSE and brain microstructure in preterm neonates. Our findings suggest that maternal PNSE is associated with alterations in the offspring’s uncinate fasciculus tract as early as term-equivalent age. More specifically, we found that increases in PNSE were associated with higher diffusivity  in the uncinate fasciculus when controlling for GA, PMA, sex, SES, maternal age, and number of days on parenteral nutrition.The limbic system contains 3 distinct, but partially overlapping, functional networks. These include the dorsomedial default mode, hippocampal-diencephalic-retrosplenial, and temporo-amydala-orbitodrontal networks et al.Recent studies provide evidence that the developing white matter is vulnerable to maternal prenatal adversity. Reduced FA in white matter areas including the uncinate fasciculus has been observed in infants of highly anxious mothers The reasons for our findings of a relationship between the microstructure of the uncinate fasciculus and PNSE, but not trait anxiety, remain unclear. A number of factors may account for this finding. A recent study into the validity of the STAI in the perinatal period suggests that the mean STAI-TR score in our sample was well below the cutoff range associated with clinically diagnosable DSM-IV anxiety disorder Although the precise mechanisms linking PNSE with neurodevelopmental outcomes in offspring have yet to be determined, research suggests that it may lead to changes in hormones and neurotransmitters in utero PNSE may also affect offspring through epigenetic mechanisms such as DNA methylation and histone modification Differences in microstructural properties of white matter tracts are typically explained in relation to differences in myelination. However, myelination in the uncinate fasciculus and inferior longitudinal fasciculus commences in the third postnatal month Preterm infants in our study were scanned at term-equivalent age and thus were more likely exposed to suboptimal nutrition, ventilation, and other early-life stressors than term-born neonates were. Furthermore, premature birth is known to be associated with altered white matter development et al.To our knowledge, this represents the largest sample in studies of prenatal stress exposure and infant brain development, as well as the first study to investigate this relationship in a preterm sample. In a recent study by Benavente-Fernández et al.A limitation of our study is that our measure of stressful life events was adapted from a validated questionnaire. However, our results are in line with existing literature on stressful life events and early brain development. Moreover, our measure of life events covers 1 year prior to the scanning session, which includes several months prior to conception. However, Scheinost Although impairments in uncinate fasciculus microstructure have been associated with behavioral and/or psychiatric outcomes in childhood and/or adolescence in term-born populations In conclusion, we provide what we believe is the first evidence that prenatal stress exposure is associated with altered development of the uncinate fasciculus in premature neonates. These findings add to a growing set of studies implicating maternal prenatal stress in early brain development and suggest that changes in white matter microstructure may be present as early as term-equivalent age.


F proposed by Kemeny and Oppenheim among them possesses symmetries and asymmetries proposed by Elles and Fitelson, monotonicity proposed by Greco et al., and normalizing property suggested by many researchers. Based on the semantic information theory, a measure b* similar to F is derived from the medical test. Like the likelihood ratio, measures b* and F can only indicate the quality of channels or the testing means instead of the quality of probability predictions. Furthermore, it is still not easy to use b*, F, or another measure to clarify the Raven Paradox. For this reason, measure c* similar to the correct rate is derived. Measure c* supports the Nicod Criterion and undermines the Equivalence Condition, and hence, can be used to eliminate the Raven Paradox. An example indicates that measures F and b* are helpful for diagnosing the infection of Novel Coronavirus, whereas most popular confirmation measures are not. Another example reveals that all popular confirmation measures cannot be used to explain that a black raven can confirm “Ravens are black” more strongly than a piece of chalk. Measures F, b*, and c* indicate that the existence of fewer counterexamples is more important than more positive examples’ existence, and hence, are compatible with Popper’s falsification thought. After long arguments between positivism and falsificationism, the verification of universal hypotheses was replaced with the confirmation of uncertain major premises. Unfortunately, Hemple proposed the Raven Paradox. Then, Carnap used the increment of logical probability as the confirmation measure. So far, many confirmation measures have been proposed. Measure  Both can be used as a major premise for a syllogism. Deductive logic needs major premises; however, some major premises for empirical reasoning must be supported by inductive logic. Logical empiricism affirmed that a universal judgment can be verified finally by sense data. Popper said against logical empiricism that a universal judgment could only be falsified rather than be verified. However, for a universal or hypothetical judgment that is not strict, and is therefore uncertain, such as “Almost all ravens are black”, “Ravens are black”, or “If a man’s Coronavirus test is positive, then he is very possibly infected”, we cannot say that one counterexample can falsify it. After long arguments, Popper and most logical empiricists reached the identical conclusion      ,C = P−P(e1)P(h1)    ,e1, h1) = P(h1|e1)−P(h1|e0)               = P(e1|h1)−P(e1|h0)             ,L = log[P(e1|h1)/P(e1|h0)]            , andF = [ P(e1|h1)−P(e1|h0)]/[ P(e1|h1)+ P(e1|h0)]    .The popular confirmation measures include:D and C proposed by Carnap are for incremental confirmation and absolute confirmation respectively. There are more confirmation measures in , , D and Ch1(x) = “x is elderly”, where x is a variable for an age and h1(x) is a predicate. An instance x=70 may be the evidence, and the truth value T(θ1|70) of proposition h1(70) should be 1. If x=50, the (uncertain) truth value should be less, such as 0.5. Let e1 = “x ≥ 60”, true e1 may also be the evidence that supports h1 so that T(θ1|e1) > T(θ1).Hypothesis 1: h1(x) = “If age x ≥ 60, then x is elderly”, which is a hypothetical judgment, a major premise, or a rule. Note that x = 70 or x ≥ 60 is only the evidence of the consequent “x is elderly” instead of the evidence of the rule. The rule’s evidence should be a sample with many examples.Hypothesis 2: e1→h1 = “If age x ≥ 60, then x is elderly”, which is the same as Hypothesis 2. The difference is that e1 = “x ≥ 60”; h1 = “x is elderly”. The evidence is a sample with many examples like {, , …}, or a sampling distribution P, where P means statistical probability.Hypothesis 3: Firstly, we need to clarify that confirmation is to assess what kind of evidence supports what kind of hypotheses. Let us have a look at the following three hypotheses: Hypothesis 1 has a (uncertain) truth function or a conditional logic probability function between 0 and 1, which is ascertained by our definition or usage. Hypothesis 1 need not be confirmed. Hypothesis 2 or Hypothesis 3 is what we need to confirm. The degree of confirmation is between −1 and 1.c:h is the major premise to be confirmed, and e is the evidence that supports h; h and e are so used by Elles and Fitelson  instead of 1.c = −c. Actually, it means c = −c for any x and y. Similarly, ES is Antecedent Symmetry, which means c = −c for any x and y. Since e and h are not the antecedent and the consequent of a major premise from their point of view, they cannot say Antecedent Symmetry and Consequent Symmetry. Consider that c becomes c. According the literal meaning of HS (Hypothesis Symmetry), one may misunderstand HS as shown in Elles and Fitelson defined HS by c = −c ES. However, it is in fact HS or Consequent Symmetry. In [F (where the right side is evidence) should have HS: F = −F, whereas F should have ES: F= −F. However, this “ES” does not accord with the original meaning of ES in [F and F possess HS instead of ES. The more serious thing because of the misunderstanding is that [c = c), as well as HS, are desirable, and hence, measures S, N, and C are particularly valuable. For example, the misunderstanding happens in ,19, wherof ES in . Both F are the antecedent and the consequent of the rule e→h. To avoid the misunderstanding, we had better replace c with c(e→h) and use “Antecedent Symmetry” and “Consequent Symmetry” instead of “Evidence Symmetry” and “Hypothesis Symmetry”. The author of this paper approves the conclusion of Elles and Fitelson that only HS  is desirable. Therefore, it is necessary to make clear that D proposed by Carnap is often referred to as the standard Bayesian confirmation measure. The above analyses, however, show that D is only suitable as a measure for selecting hypotheses instead of a measure for confirming major premises. Carnap opened the direction of Bayesian confirmation, but his explanation about D easily lets us confuse a major premise’s evidence (a sample) and a consequent’s evidence (a minor premise).Measure p(h|e) as Bayesian confirmation measures, those with P(e|h) as Likelihoodist confirmation measures, and those for h→e as converse Bayesian/Likelihoodist confirmation measures. This division is very enlightening. However, the division of confirmation measures in this paper does not depend on symbols, but on methods. The optimized proportion of the believable part in the truth function is the channel confirmation measure b*, which is similar to the likelihood ratio, reflecting how good the channel is. The optimized proportion of the believable part in the likelihood function is the prediction confirmation measure c*, which is similar to the correct rate, reflecting how good the probability prediction is. The b* may be called the logical Bayesian confirmation measure because it is derived with Logical Bayesian Inference [P(e|h) may be used for b*. The c* may be regarded as the likelihoodist confirmation measure, although P(h|e) may be used for c*. Greco et al.  call connference , although→e. Confirmation measures b*(e→h) and c*(e→h) are related to misreporting rates, whereas converse confirmation measures b*(h→e) and c*(h→e) are related to underreporting rates. This paper also provides converse channel/prediction confirmation measures for rule CF, was proposed by Shortliffe and Buchanan for a backward chaining expert system [h→e is. The relationship between measures CF and Z is CF(h→e) = Z(e→h) [The Certainty Factor, which is denoted by t system . It indi= Z(e→h) .As pointed out by Heckerman and Shortliffe , the Cerb*(e1→h1) is related to the believable part of the truth function of predicate e1(h). It is similar to CF(h1→e1). The differences are that b*(e1→h1) is independent of P(h) whereas CF(h1→e1) is related to P(h); b*(e1→h1) is compatible with statistical probability theory whereas CF(h1→e1) is not.Measure b* or c* as the Certainty Factor to simplify belief-networks or probabilistic expert systems? This issue is worth exploring.Is it possible to use measure F, b*, and c* can reflect the importance of counterexamples. In Example 1 of P(e1|h1) (for b*) or P(h1|e1) (for c*). In Example 2 of Popper affirms that a counterexample can falsify a universal hypothesis or a major premise. However, for an uncertain major premise, how do counterexamples affect its degree of confirmation? Confirmation measures F, b*, and c* are compatible with Popper’s falsification thought. Popper affirms that a counterexample can falsify a universal hypothesis, which can be explained by that for the falsification of a strict universal hypothesis, it is important to have no counterexample. Now for the confirmation of a universal hypothesis that is not strict or uncertain, we can explain that it is important to have fewer counterexamples. Therefore, confirmation measures c*(e1→h1) is what they need.Scheffler and Goodman  proposedb*(e →h) and c*(e →h). The measure b* is similar to the measure F proposed by Kemeny and Oppenheim; it can reflect the channel characteristics of the medical test like the likelihood ratio, indicating how good a testing means is. Measure c*(e→h) is similar to the correct rate but varies between −1 and 1. Both b* and c* can be used for probability predictions. The b* is suitable for predicting the probability of disease when the prior probability of disease is changed. Measures b* and c* possess symmetry/asymmetry proposed by Elles and Fitelson [Using the semantic information and statistical learning methods and taking the medical test as an example, this paper has derived two confirmation measures Fitelson , monotonFitelson , normaliF and b* and the like, which are the functions of likelihood ratio, can. It has also proved that popular confirmation measures did not support the conclusion that a black raven could confirm more strongly than a non-black non-raven thing, such as a piece of chalk. It has shown that measure c* could definitely deny the Equivalence Condition and exactly reflect Nicod–Fisher Criterion, and hence, could be used to eliminate the Raven Paradox. The new confirmation measures b* and c* as well as F indicates that fewer counterexamples’ existence is more important than more positive examples’ existence; therefore, measures F, b*, and c* are compatible with Popper’s falsification thought. This paper has shown that most popular confirmation measures cannot help us diagnose the infection of COVID-19, but measures When the sample is small, the degree of confirmation calculated by any confirmation measure is not reliable, and hence, the degree of confirmation should be replaced with the degree interval of confirmation. We need further studies combining the theory of hypothesis testing. It is also worth conducting further studies ensuring that the new confirmation measures are used as the Certainty Factors for belief-networks.


P = 0.033); the lateralization index of the betweenness centrality revealed that the medial fronto-orbital gyrus (P = 0.008), superior temporal gyrus (P = 0.031), and hippocampus (P = 0.028) showed significantly increased leftward asymmetry in preterm infants relative to term-infants independent of sex, age at imaging, and bronchopulmonary dysplasia. The altered lateralization of fronto-limbic brain circuitry might be involved in the early development of social–emotional disorders in preterm infants.This study aimed to elaborate upon prior findings suggestive of the altered lateralization of structural connectivity in the developing preterm brain by using diffusion tensor imaging tractography to explore how network topological asymmetries in fronto-limbic neural circuitry are altered at 36–41 weeks, postmenstrual age in 64 preterm infants without severe brain injury and 33 term-born infants. We compared the pattern of structural connectivity and network lateralization of the betweenness centrality in the medial fronto-orbital gyrus, superior temporal gyrus, amygdala, and hippocampus—the structures comprising the fronto-limbic brain circuit—between preterm and term infants. Global efficiency, local efficiency, and small-world characteristics did not differ significantly between the two hemispheres in term-born infants, suggesting that integration and segregation are balanced between the left and right hemispheres. However, the preterm brain showed significantly greater leftward lateralization of small-worldness ( Moreover, why some preterm birth induces adverse long-term neurodevelopmental effects despite exhibiting no measurable focal brain injury on magnetic resonance imaging (MRI) remains unclear.Significant recent improvements in the capacity of the neonatal intensive care unit to administer preterm care have engendered a burgeoning interest in the neurodevelopment of premature infants. Accumulating evidence indicates that such children suffer from multiple neurodevelopmental disorders; indeed, this population features 75% and 7% prevalence of developmental disabilities and autism spectrum disorders (ASD), respectively4. However, while we have previously shown that preterm infants exhibit significantly lower global and local efficiency, increases in small-worldness indicate an enhanced resilience against prematurity-related pathology5.Recent studies using advanced neuroimaging techniques have revealed meaningful differences in white matter microstructure and structural brain networks between preterm infants and term-born neonates. Specifically, preterm infants were observed to exhibit aberrant structural connectivity, including global efficiency, local efficiency, and small-worldness, in major tracts connecting the corticospinal tracts, commissural fibres, and cerebellum6, the impairment of which is associated with premature birth even in the absence of focal brain injury7. While Kwon et al.8 demonstrated that preterm neonates show a significant lack of lateralization in regions involving both receptive and expressive language, the mechanisms mediating social–emotional processing remain poorly characterized.Cerebral lateralization refers to the functional specialization of the two cerebral hemispheres: e.g., language production and motor functions are lateralized to the left cerebral hemisphere and emotional processing to the right. This spatial segregation of dynamic brain networks is reportedly a critical process in early neurodevelopment10. A previous study of 6-month-old healthy infants demonstrated that the rightward lateralization of the fronto-limbic brain development of white matter microstructure helps to support the development of social communication skills11. Other research performed across the past decade suggests that altered lateralization of the neural networks comprising the fronto-limbic circuit contributes to social and emotional problems in children and adults14. To date, however, no study has investigated cerebral lateralization in the fronto-limbic connection of the structural brain networks of preterm neonates. To address this dearth in the literature, we studied the asymmetry of both global and local networks of the fronto-limbic circuit in the neonatal brain using diffusion tensor imaging (DTI) tractography and structural network analysis. We hypothesized that preterm birth results in architectural changes in the global network or specific fronto-limbic connections at near-term age. Furthermore, we speculated that the altered lateralization of preterm brain in fronto-limbic connection might be related to social–emotional scores at 18 months of corrected age in the developing preterm brain.The aberrant network connectivity of the fronto-limbic circuit has been implicated in emotion, social communication, psychiatric, and neurodevelopmental disordersP < 0.001) and lower birth weight . Preterm infants exhibited significantly lower 1-min and 5-min Apgar scores. With respect to comorbidities, grade 1 intraventricular haemorrhages (P = 0.054), culture-proven sepsis (P = 0.102), and bronchopulmonary dysplasia (P < 0.001) were more frequent among preterm infants. Neurodevelopmental outcome data were available at 18 months of age (corrected for the prematurity) for 57 out of 64 (89%) of the preterm infants and for 21 out of 33 (64%) of the term infants. The preterm infants had significantly lower mean composite scores on cognitive , motor  and social–emotional  domains compared with term-born infants. The incidence of language delay was significantly higher among preterm infants than term infants (42.1% vs. 10%). Thirteen  preterm infants had a Bayley-SE score of < 85—i.e., > 1 SD below the standardized mean for the assessment. No term infant had a Bayley-SE score of < 85.Table P > 0.05). The small worldness tended to be higher in preterm infants than term-born infants. We quantified the betweenness centrality for the fronto-limbic connections: the medial fronto-orbital gyrus (mFOG), superior temporal gyrus (STG), amygdala, and hippocampus. However, the betweenness centrality of the mFOG , STG , and hippocampus  in the left hemisphere tended to be higher in preterm infants than in term-born infants independent of age at MRI and bronchopulmonary dysplasia.We compared the differences in local efficiency, clustering coefficient, global efficiency, path length, and the small-worldness between preterm infants and term-born neonates. These measures were comparable between the two groups (Table P = 0.033).The results of the statistical analysis of the lateralization index of the global network metrics are summarized in Fig. P = 0.008). The rightward predominance of the betweenness centrality in the STG and hippocampus of term-born controls was not observed in preterm infants. When comparing the lateralization index of the fronto-limbic cortex between preterm and full-term infants, the mFOG , STG , and hippocampus  of preterm infants was observed to exhibit significantly greater leftward asymmetry after correcting for sex, age at MRI, and bronchopulmonary dysplasia.Figure P = 0.044) and hippocampus (P = 0.032) between preterm infant with normal and abnormal Bayley-SE.Our assessment of how the age at MRI scan affects the asymmetry of the global network, mFOG, STG, amygdala and hippocampus at 36–41 weeks of corrected age revealed no correlation between age at MRI scan and global network or fronto-limbic asymmetries in all infants (n = 97)  The relatively small sample size in term-born neonates may have resulted in insufficient power to detect possible associations. This is partially due to the high cost of data collection and the substantial lack of term-born neonates for follow-up. (2) The confirmation of an association between socio-emotional development and fronto-limbic asymmetry was limited by our small number of preterm infants with low socio-emotional scores and by the preterm infants not having been able to undergo more assessments for the subdomain of behavior at older ages. (3) We analyzed the development of the central nervous system in children at an age when it is undergoing constant change, and its correlation with clinical development is highly dynamic. Hence, the development or alterations of the nervous systems of the included preterm infants may have been delayed at the point of observation—despite our having collected data at a corrected age of 18 months after birth. The value of the correlative analysis between anatomical and functional findings should be assessed by larger prospective cohort studies.The present study benefitted from recruited a large number of preterm infants with available 3 T MRI imaging data at near-term age, allowing for the exploration of distinct white matter tracts, rather than the WM microstructure in its entirety, in relation to socio-emotional development in children born preterm. We focused on infants without significant brain injuries, using network topological asymmetries in fronto-limbic neural circuitry. Our results may implicate the alteration of fronto-limbic brain circuitry in the early development of social–emotional disorders in preterm infants. The present study further indicates that the altered interhemispheric leftward asymmetries of small-worldness in the global network and fronto-temporo-limbic areas may help to elucidate the etiology of neurodevelopmental disorders across the long-term follow-up of preterm infants.The present study recruited 83 preterm infants from the Hanyang Inclusive Clinic for Developmental Disorders, Hanyang University Seoul Hospital, between December 2016 and April 2018. Our sample included infants with no evidence of genetic disorders, congenital infections, or congenital brain abnormalities, and for whom MRI data obtained at near-term age  was available. We excluded eight infants with (1) intraventricular or intracranial hemorrhage greater than grade I on brain ultrasound at 3 days, 7 days, or 3 weeks of birth; (2) periventricular leukomalacia or white matter injury on term-equivalent MRI; or (3) intrauterine growth retardation. Of the 83 eligible preterm children, 75 met our inclusion criteria. 33 term-born infants  with normal MRI findings and neurodevelopment were also recruited for the term-born control group. Eleven of the remaining 75 preterm infants were removed due to poor image quality that precluded DTI processing; hence, the initial sample included 64 datasets. Our final sample of preterm infants and term-born infants included 64 and 33 datasets, respectively. Images were obtained from the controls and preterm infants during natural sleep without sedation. The infants underwent follow-up neurosensory and neurodevelopmental assessments at 4, 9, and 18 months of corrected age. Demographic data included maternal details, GA, birth weight, sex, and Apgar scores. Neonatal outcomes included patent ductus arteriosus, culture-proven sepsis, necrotizing enterocolitis, retinopathy of prematurity, and bronchopulmonary dysplasia. This present study protocol and scanning procedure were approved by the institutional review board of Hanyang University Hospital (IRB No. 2017-04-004-002). All procedures were performed in accordance with the principles of the Declaration of Helsinki, and informed consent for participation was obtained from the children's parents prior to their participation in the study.24, were used to perform the neurological and developmental assessments at Hanyang Inclusive Clinic for Developmental Disorders when the children were 18 months of age (corrected for prematurity). The routine neurological examination was completed by a developmental pediatrician, while the developmental assessment was performed by paediatric physiotherapist and neonatologist. Seven of the preterm infants (11%) did not complete the Bayley-III test at 18 months of corrected age; the parents of three of the infants did not permit the assessment of their children; two infants dropped out of the study during follow-up, and two of the participants were withdrawn from the study because of their inability to cooperate with testing.The Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III)Developmental outcomes included cognitive, language, motor, and social–emotional composite BSID-III scores based on the child’s adjusted age at the time of evaluation. Assessments of the cognitive, language, and motor domains were performed using the items administered to the child, while assessments of social–emotional domains were informed by the primary caregiver’s responses to a questionnaire. The normative means (standard deviation [SD]) of each outcome score was 100; scores of less than 85 (1 SD below average) were considered to indicate neurodevelopmental delay.All scans were performed on 3 T MRI . The T1-weighted images included sagittal and axial T1 spin-echo sequences  and axial T2 Turbo spin-echo sequences (3000/100/1). DTIs were obtained using a single-shot spin-echo planar sequence with a SENSE factor of two and an echo-planar imaging factor . Diffusivities were measured along 15 directions with an electrostatic gradient model (b = 800). No sedation was used and the infants remained asleep in the scanner by feeding them before the scan. A pulse oximeter was monitored to ensure stable heart rate and respiration.http://www.fmrib.ox.ac.uk/fsl in the public domain). Motion artifacts and eddy current distortions were corrected by normalizing each diffusion-weighted volume to the nondiffusion-weighted volume (b0) with the FMRIB Linear Image Registration Tool. Each brain mask was created using the Brain Extraction Tool (BET). Subsequently, DTIs were reconstructed for each voxel using DTIFit in FMRIB’s Diffusion Toolbox. Fractional anisotropy (FA) and mean diffusivity (MD) values were then calculated. Whole-brain deterministic diffusion fiber tracking was performed for each infant using quantitative anisotropy in DSI Studio (dsi-studio.labsolver.org) and was estimated using a streamlined algorithm in DSI Studio25. Fiber tracking was initiated from the center of each voxel and proceeded to adjacent voxels by following the direction of water diffusivity. Fiber tracking was stopped when the FA value fell below 0.1 and if the streamline turned with a curvature angle of more than 45°. Every step of image processing was performed in a blind way and MRI images with spurious brain connections due to possible noise effects on the whole brain network were excluded for network construction and statistical analyses.The diffusion-weighted images were processed using the FMRIB Software Library (27. Network nodes were defined based on the Johns Hopkins University (JHU) neonate atlas. All neonatal FA images were nonlinearly transformed to the JHU neonate atlas, and labels in the JHU atlas space were inversely transformed to FA images using the inverse warping field. The 64 labels in the JHU neonate atlas were defined as nodes for an individual structure network. We used estimated fiber tracts in the whole brain for edge definition and quantified connectivity between cortical regions. The connectivity strength was then computed by obtaining the average FA value along the fiber tracts4. The average FA value was calculated by the arithmetic average of FA taken over all voxels comprising each fiber and then averaged over all fibers comprising the tract. Finally, we constructed unweighted 64 × 64 connectivity network matrices for each subject ect Fig. .Figure 5p) and global efficiency (Eglob): a shorter path length and increased global efficiency suggest the potential integration of effective connections. The characteristic path length was defined by the average shortest path length in a network28.The global and local efficiency, characteristic path length, and small-worldness were computed to characterize the potential ease with which information could be transferred concurrently across a network and locally communicated neighborhood. The network integration refers to how efficiently and rapidly all the nodes integrate discrete specialized information. The most popular network integration measures include characteristic path length .The local efficiency of a node was calculated as the global efficiency of the neighborhood sub-network of this node. The local efficiencies across all nodes were averaged to estimate the local efficiency of the network31, a regions-of-interest (ROI) connectivity analysis was performed to explore fronto-temporo-limbic regions, including the bilateral mFOG, STG, amygdala, and hippocampus  with an alpha of 0.05, a desired power of 0.8, and a dropout rate 10%. For the term infants, the total sample size was smaller than the calculated sample due to lack of follow-up assessment. General linear models were generated to compare the network parameters and lateralization indices of the network parameters between preterm and full-term infants. The analysis of structural connectivity incorporated the global network matrix, including clustering, local efficiency, path length, global efficiency, and small-worldness, and the betweenness centrality of the fronto-limbic connections. A correlation analysis was performed to explore whether the age at MRI scan correlated with the lateralization index of the structural network. The subgroup analyses for the comparisons of structural network metrics between 57 preterm infants with abnormal Bayley-SE (< 1SD below average) and normal Bayley-SE (≥ 1SD below average) were performed using clinical variables such as gestational age and age at scan as covariates.SPSS 21.0  software was used for statistical calculations. We used Student’s t-test, Mann–Whitney U-test, Fisher’s exact test, and the chi-square analysis to compare clinical variables between groups. Based on the prevalences of impaired cognition or motor outcomes among very-low birth weight newborns (15% among term-born children and 40% among very preterm children)17. By incorporating the relative network metrics of both hemispheres into one value, the AS(X) index permits the assessment of differences between the right and left hemispheres. Ranging between + 100 and − 100, the AS(X) is positive when metric X shows prominent rightward asymmetry and negative when the opposite is the case. Lw measures the overall routing efficiency of the network and is inversely related to Eglobal. For a network, the higher the Lw, the less efficient the global integration. Hence, positive AS(Lw) indicates a leftward advantage of global integration, and negative AS(Lw) a rightward asymmetry of global integration. Continuous variables are represented as the mean ± SD. A P value of ≤ 0.05 was considered to indicate statistical significance. The resulting P values were corrected for the false discovery rate (FDR); FDR-adjusted P values of < 0.05 were considered to indicate statistical significance for multiple comparisons.The asymmetry of the aforementioned network metrics was evaluated with the asymmetry score: AS = 100 × (X(L) − X(R))/(X(L) + X(R)), where X(R) and X(L) indicate the network parameters of the right and left hemispheres, respectively


The exact onset of brain injury in infants with congenital heart disease (CHD) is unknown. Our aim was, therefore, to assess the association between prenatal Doppler flow patterns, postnatal cerebral oxygenation and short-term neurological outcome.cSO2) and fractional tissue oxygen extraction (FTOE) were assessed during the first 3 days after birth, and during and for 24 hours after every surgical procedure within the first 3 months after birth. Neurological outcome was determined preoperatively and at 3 months of age by assessing general movements and calculating the Motor Optimality Score (MOS).Prenatally, we measured pulsatility indices of the middle cerebral (MCA-PI) and umbilical artery (UA-PI) and calculated cerebroplacental ratio (CPR). After birth, cerebral oxygen saturation (rP = 0.04), UA-PI . Infants with abnormal MOS had lower MCA-PI (P = 0.02) and CPR (P = 0.01) and higher UA-PI at the last measurement (P = 0.03) before birth. In infants with abnormal MOS, rcSO2 tended to be lower during the first 3 days after birth, and FTOE was significantly higher on the second day after birth (P = 0.04). Intraoperative and postoperative rcSO2 and FTOE were not associated with short-term neurological outcome.Thirty-six infants were included. MOS at 3 months was associated with MCA-PI (rho 0.41, In infants with prenatally diagnosed CHD, the prenatal period may play an important role in developmental outcome. Additional research is needed to clarify the relationship between preoperative, intra-operative and postoperative cerebral oxygenation and developmental outcome in infants with prenatally diagnosed CHD. Up to 50% of infants with congenital heart disease (CHD) have neurodevelopmental impairments later in life . As a coPrenatally, circulatory alterations and impaired brain maturation have been frequently observed . FetusesAfter birth, many of the prenatal findings, such as a smaller head circumference, lower total brain volumes, and an altered cerebral metabolism persist ,17–21. FSurgical procedures and the postoperative period pose an additional threat to the young brain. Ischemia and reperfusion injury, hypothermia, inflammatory and immune responses, altered cerebral blood flow regulation and decreased cardiac output might all contribute to brain injury during and after surgery ,31. New Currently, it is still unknown which period is the most threatening for the young developing brain in infants with CHD. To date, no study has assessed cerebral abnormalities and its relation with neurological outcome from before birth to the postoperative period in these infants. Our aim was, therefore, to perform a longitudinal assessment of prenatal Doppler flow patterns and postnatal, intraoperative and postoperative cerebral oxygen saturation and extraction in relation to short-term neurological outcome in infants with CHD that were admitted to the intensive care (ICU) immediately after birth.A prospective observational cohort study (registration number: NTR5523) was conducted at the fetal medicine unit, the NICU, congenital heart center and pediatric intensive care unit of the University Medical Center Groningen. Between May 2014 and August 2016, all fetuses with isolated CHD expected to require ICU admission immediately after birth were prenatally enrolled when parental informed consent was obtained. After birth, infants were echocardiographically assessed by a pediatric cardiologist to confirm the cardiac diagnosis. Infants were excluded from further participation if cardiac diagnosis could not be confirmed, if born before a gestational age of 36 weeks, or in case of major chromosomal, genetic or structural anomalies that became apparent after birth. This study was approved by the Medical Ethical Committee of the University Medical Center Groningen METc number: METc2014/083.Neurological outcome was assessed by general movements (GMs) at two different moments. Preoperatively, at the age of 7 days (5–10 days), a video recording of 30–60 minutes was made. Postoperatively, at the age of 3 months, a video recording of approximately 10 minutes was made . During The GMs were scored independently by two different assessors (MJM and AFB) and one of them was unaware of the clinical condition of the patient (AFB). Both observers are certified as advanced scorers by the GM Trust. At the age of 7 days, the GMs were scored as either normal or abnormal. Abnormal GMs included poor repertoire, chaotic, and cramped synchronized movement patterns. Furthermore, we explored more detailed aspects of the motor repertoire reflected in a motor optimality score (MOS). At this age, the MOS ranges from 8 (poor) to 18  and MOS <15 was considered to be abnormal. At the age of 3 months we assessed the presence and quality of fidgety movements . Furthermore, we assessed the MOS which ranges from 5 (poor) to 28  at this age and MOS <25 was considered to be abnormal –36.During pregnancy, pulsatility indices of the middle cerebral artery (MCA-PI) and umbilical artery (UA-PI) were measured repeatedly by an experienced fetal medicine expert (CMB) and cerebroplacental ratio (CPR) was calculated. All Doppler parameters were converted into Z-scores to adjust for differences in gestational age at fetal examinations. For statistical purposes, the last available Doppler measurement before birth was used.cSO2) was measured with the INVOS 5100c spectrometer  with neonatal sensors (Medtronic), placed on the frontoparietal side of the forehead. First, rcSO2 was measured daily for at least two consecutive and stable hours during the first 3 days after birth starting immediately after stabilization of the infant on the NICU. Preferably, the measurements were performed at the same time each day to avoid possible effects of diurnal variation. Second, rcSO2 was measured during every corrective/palliative surgical procedure within the first 3 months after birth. Third, rcSO2 was measured for 24 hours following each invasive cardiac procedure within the first 3 months after birth. Simultaneously with rcSO2 measurements, we measured preductal arterial oxygen saturation (SpO2) and calculated cerebral fractional tissue oxygen extraction (FTOE). For statistical purposes, we selected representative two-hour periods of stable rcSO2 measurements, preferably at the same time during the day, and calculated mean rcSO2 and FTOE for each of the first 3 days after birth. In addition, we calculated mean rcSO2 and FTOE during cardiac surgery and for 24 hours after cardiac surgery. Furthermore, we assessed the burden of hypoxia in two ways (percent of time <60% and percent of time <50%) and rcSO2 nadir during cardiac surgery.After birth, cerebral oxygen saturation  and for graphical display GraphPad Prism version 5 was used. Data are presented as either median (range) or number (percentage). First, we used descriptive statistics to visualize the association between fetal Doppler flow patterns, postnatal, intraoperative and postoperative r or >90% –38. The ff value . The higInitially, 45 fetuses with CHD were included between May 2014 and August 2016 . After bPreoperatively, GMs were recorded in 25 infants. Fourteen infants (56%) had abnormal GMs. They all scored poor repertoire, none had chaotic or cramped synchronized movements. Motor optimality score was 13 (10–18). In eleven infants, GMs were not recorded due to scheduled surgery prior to the age of five days (n = 4), immobility due to sedation (n = 1), transfer to another hospital within five days after birth (n = 3) and mortality (n = 3).At the age of three months, GMs were recorded in 29 infants. Two infants had absent fidgety movements and one had abnormal fidgety movements. Motor optimality score was 26 (11–28). Based on the cut-off point of <25, twelve infants (41%) had abnormal motor optimality scores. Reasons for missing recordings at this age were mortality before the age of three months (n = 6) and withdrawal from study participation (n = 1).P = 0.25). Two of the three infants with deteriorating neurological outcome had a complicated postoperative course. One infant had low cardiac output syndrome and the other infant developed a cardiac tamponade needing a re-intervention.Twenty-one infants had GMs assessment at both ages. Of these infants, ten remained stable, three deteriorated and eight improved . McNemarP = 0.35) and CPR (-0.65 (-4.06 to 2.80), P = 0.01) and slightly higher UA-PI Z-scores (0.44 (-1.74 to 4.01), P = 0.02).Prenatal Doppler flow patterns were assessed in all infants. Gestational age at the last fetal examination was 34.4 (21.1–39.1) weeks. In comparison with reference values of healthy fetuses, we found slightly lower MCA-PI (-0.19 (-3.94 to 2.59), cSO2 increased from 61% (32%-89%) to 70% (52%-91%) during the first 3 days after birth. On day 1, six neonates had rcSO2 values <50%, which could be explained by clinical conditions . All neonates stabilized during the first 3 days and none of the neonates had rcSO2 values <50% on day 3 after birth. Twenty-six neonates (72%) had at least one cardiac surgical procedure within the first 3 months after birth. During surgical procedures, rcSO2 was lower in comparison with the first 3 days after birth (53% (36%-69%)). Median duration of surgery was 7.0 (1.5–12.3) hours. The median burden of hypoxia <60% and <50% was 68% (4%-100%) and 34% (0%-95%) of the recording time, respectively. The median rcSO2 nadir during surgery was 22% (15%-56%). Following cardiac surgery, rcSO2 increased to 61% (42%-78%).After birth, rP = 0.04) and CPR  positively correlated with MOS, while UA-PI negatively correlated  with MOS at 3 months of age. There were no significant correlations between postnatal, intraoperative  and postoperative rcSO2 or FTOE and MOS at the age of 3 months.Prenatal Doppler flow patterns strongly correlated with MOS at 3 months of age. Pulsatility index of the middle cerebral artery , higher UA-PI (P = 0.03) and lower CPR (P = 0.01) in comparison with infants with normal MOS at the age of 3 months. Cerebral oxygen saturation during the first 3 days after birth was lower in infants with abnormal MOS, however, this did not reach statistical significance. Both during and after surgical procedures rcSO2  was similar in infants with normal and infants with abnormal MOS. In infants with abnormal MOS, FTOE tended to be higher during the first three days after birth, reaching statistical significance on day 2 (P = 0.04). During and after cardiac surgical procedures, there were no associations between FTOE and short-term neurological outcome. The number of interventions was also not associated with GMs at an age of 3months (P = 0.12).Results were similar for abnormal and normal MOS . InfantsP = 0.02).Infants with a combination of abnormal prenatal and postnatal cerebral values were more likely to have abnormal MOS in comparison with infants that had no abnormal cerebral values or only at one of both periods  ,45. One Our results indicate that Doppler patterns indicative for compensatory “brain sparing” mechanisms during fetal life actually suggest that oxygen delivery to the brain is insufficient to meet metabolic demands in fetuses with CHD. This may lead to impaired brain maturation, a finding that has been commonly reported in fetuses and neonates with CHD –15. The cSO2 during the first 3 days was consistently lower and FTOE higher in infants with abnormal MOS at 3 months of age, we were unable to demonstrate a statistically significant association between preoperative cerebral oxygenation and developmental outcome. It might be that there is no association between preoperative oxygenation and short-term neurological outcome in infants with prenatally diagnosed CHD. We assessed neurological outcome at 3 months of age using the best available method at present, with a high sensitivity and specificity. We speculate that the brain may, at least transiently, be able to recover from hypoxic-ischemic events suffered immediately after birth. Brain development continues throughout childhood and involves not only the onset of new pathways and connections, but also elimination of others [cSO2 is associated with developmental outcome, but that our sample size was too small to reach statistical significance. Previous studies on the association between preoperative rcSO2 and neurodevelopmental outcome in infants with CHD, however, support our first explanation, as they were also unable to demonstrate a clear association between preoperative rcSO2 and neurodevelopmental outcome [While rf others . Due to  outcome –50.cSO2 and various domains of neurodevelopmental outcome, rcSO2 was never a strong predictor of neurodevelopmental outcome [Cerebral oxygen saturation and FTOE during and after cardiac surgery were not associated with short-term neurological outcome. This is in line with previous literature–51. Alth outcome –52. AdvaAs we found that infants with abnormal perfusion or oxygenation both prenatally and postnatally had a nine-fold increased risk of having abnormal short-term neurological outcome, we suggest that there is a cumulative effect of hypoxic-ischemic events in infants with prenatally diagnosed CHD. This multiple-hit theory has been previously described in very preterm born infants . AbnormaThis study has several strengths and limitations. The longitudinal design from prenatal diagnosis until the age of 3 months is unique and important since infants with CH D are at risk of developing brain injury at various moments during early life. The observational design, however, implied that some of the values were missing, which might have induced selection bias. Other limitations were the relatively small sample size and the heterogeneity of the study population. The included types of CHD might have different effects on cerebral oxygenation and perfusion and they are associated with a different postnatal course . The infants with a more intense treatment course might have had less opportunity to develop certain age-appropriate skills. This could have influenced GMs at an age of 3 months. Nonetheless, our study population is a representative sample of an ICU and all included lesions have been associated with neurodevelopmental impairments later in life . FurtherIn conclusion, based on our findings we hypothesize that the prenatal period may play an important role in developmental outcome in infants with CHD that were admitted to the ICU immediately after birth. Additional research is needed to clarify the association between cerebral oxygenation during the first days after birth and neurological outcome in infants with CHD.S1 TableMA, mitral atresia; AA, aortic atresia.(DOCX)Click here for additional data file.S2 TablecSO2, cerebral oxygen saturation; FTOE, cerebral fractional tissue oxygen extraction. * indicates P-value <0.05.Data are presented as median (range). MOS, motor optimality score; MCA-PI, pulsatility index of the middle cerebral artery; UA-PI, pulsatility index of the umbilical artery; CPR, cerebroplacental ratio; r(DOCX)Click here for additional data file.S1 Data(SAV)Click here for additional data file.