This was supported by simulation analysis, which revealed that DTI-derived parameters with lower b-value datasets suffered from errors due to heterogeneity of cerebrospinal fluid fraction and partial volume. These findings suggest that high b-value DTI redundantly parallels with NODDI-based cortical neurite measures, but the conventional low b-value DTI is hard to reasonably characterize cortical microarchitecture.Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are widely used models to infer microstructural features in the brain from diffusion-weighted MRI. Several studies have recently applied both models to increase sensitivity to biological changes, however, it remains uncertain how these measures are associated. Here we show that cortical distributions of DTI and NODDI are associated depending on the choice of b-value, a factor reflecting strength of diffusion weighting gradient. We analyzed a combination of high, intermediate and low b-value data of multi-shell diffusion-weighted MRI (dMRI) in healthy 456 subjects of the Human Connectome Project using NODDI, DTI and a mathematical conversion from DTI to NODDI. Cortical distributions of DTI and DTI-derived NODDI metrics were remarkably associated with those in NODDI, particularly when applied highly diffusion-weighted data (b-value = 3000 sec/mm In most cases, summary parameters of DTI, fractional anisotropy (FA) and mean diffusivity (MD), have been studied based on dMRI data acquired with low b-value , however, these parameters have not been shown to be specific to underlying microstructural features of axons and dendrites (collectively referred to as neurites) and are often sensitive to tissue compartments other than neurites4.The diffusion motion of water molecules in brain tissue is affected by the local microarchitecture, including axons, dendrites and cell bodies. Diffusion tensor imaging (DTI) is a well-established model that describes Gaussian properties of diffusion motion in a fibrous structure like brain white matter from diffusion-weighted MRI (dMRI)5. NODDI models dMRI signals by combining three tissue compartments: neurites, extra-neurites, and cerebro-spinal fluid (CSF), each with different properties of diffusion motion, and enables in vivo estimation of a neurite density index (NDI) and an orientation dispersion index (ODI), as well as a volume fraction of isotropic diffusion (Viso). NODDI requires dMRI data to be scanned with multiple b-values (e.g. b = 700 and 2000 sec/mm2) and relatively higher number of diffusion gradient directions (e.g. > 90 directions over two b-shell) as compared with DTI5. The NDI estimates the volume fraction of neurites, including both axons and dendrites, whereas the ODI estimates the variability of neurite orientation: ranging from 0  to 1 . NODDI has already been applied to many studies because of their feasibility. The variation of NODDI estimates in white matter have been related to aging11 and neurologic disorders14. Of note, NODDI has proven to be useful for gray matter neurite changes as reported in several clinical studies, e.g. in patients with IFN-α-induced fatigue15, Wilson’s disease16, cortical dysplasia17, aging18, and schizophrenia19. We recently optimized NODDI for cortical gray matter20, finding that the NODDI reflects neurobiology of cortical microarchitecture – cortical distribution of NDI is closely related to cortical myelin21 and ODI is associated with cortical organization of radial/horizontal fibers23. Although there is recent debate about oversimplified assumptions in NODDI such as uniform diffusivity24, it is of note that accumulated histological evidence indicates that NDI and ODI of neural tissues are reasonably representing histology-based neurite density25 and orientation dispersion28, respectively.One recent advance for estimating the microstructural complexity of brain tissue using dMRI is the Neurite Orientation Dispersion and Density Imaging (NODDI)29, multiple sclerosis25, and stroke30. Our previous study also revealed that strong relationships between NODDI and DTI parameters in the cortex of healthy subjects, in particular, NDI and 1/MD were very highly correlated (R = 0.97)20 when used three-shell dMRI data including high b-value, but not so highly correlated when used low b-value data. On the other hand, in vitro study showed that slower-decaying component found by high b-value dMRI signals were originated from intra-neurite water31, thus suggesting that MD obtained at high b-value is specifically reflecting neurite properties. High b-value DTI in clinical studies also implicate higher sensitivity to neurobiological changes than low b-value, e.g. the contrast between gray/white matter32, ischemic/hypoxic changes in the gray matter33, white matter disintegrity in schizophrenia34 and maturation in juveniles35. However, there is no consensus how DTI is associated with NODDI parameters, and how it is dependent on the b-shell scheme.Recently, there is accumulating evidence of combined DTI and NODDI analysis in clinical studies. Those performed both DTI and NODDI in the pathological cortex all showed opposite changes between MD and NDI in Parkinson’s disease36. We used the preprocessed dMRI data from Human Connectome Project (HCP), and estimated b-shell scheme dependency of the relationship between DTI and NODDI parameters. Since the function relies on the assumption that CSF compartment (Viso) is negligible in the tissue36, we also estimated Viso in the cortex and the white matter based on previous work20 and estimated effect on the partial voluming and the relationship between DTI and NODDI parameters. We performed simulation analysis in terms of b-value, proportion of CSF signal. Our main purpose is to highlight the neurite properties in the specific subtype of brain, cortical gray matter, in healthy subjects, and investigate how DTI specifically represent the cortical NODDI metrics. We also review the past literature which applied NODDI and DTI in vitro and in vivo dMRI studies and discuss the potential interpretability of DTI.In this study, we investigated how DTI and NODDI parameters are related with each other in cortical gray matter in healthy subjects. The measures were correlated by two methods and also analyzed by utilizing a recently derived mathematical function, which converts DTI to NODDI parametershttps://www.humanconnectome.org/) were used. In particular, dMRI datasets with different b-shell structures were analyzed to investigate how the b-shell scheme affects the relationship between two diffusion model. To clarify why their relationship depends on the diffusion scheme, we also performed simulation analysis addressing several error sources such as CSF signals in dMRI data and partial volume effects. Data analyses were performed at RIKEN, and the use of HCP data in this study was approved by the institutional ethical committee (KOBE-IRB-16-24).In order to comprehensively investigate the relationship between NODDI and DTI in cortical gray matter, we examined whether NODDI parameters can be accurately estimated from DTI using their mathematical relation. Publicly available MRI data from 456 healthy subjects (aged 22–35 years) the HCP 38. The dMRI data obtained with TR = 5520 ms and TE = 89.5 ms included 270 volumes with 90 volumes for each of the three shells of b-values  in addition to 18 non-diffusion weighted (b = 0 s/mm2) volumes. From this dataset, 456 healthy subjects  scanned with a complete dataset of 270 volumes were chosen, and 49 subjects were excluded based on incomplete dMRI scans. In our previous study, NDI and the reciprocal of MD (1/MD) showed very similar surface distributions when all of the dMRI data were used, but they did not show similar distributions when only a single shell of b = 1000 dMRI data was used20. Therefore, we hypothesized that the relationship between DTI and NODDI may differ depending on the b-shell scheme of dMRI data. To address this, datasets with different b-shell schemes were used for analysis . This linear DTI model was applied not only to a single-shell ‘standard’ b = 1000 data but also to high b-value and multi-shell dMRI datasets. The primary reason of ‘forced’ fitting of DTI to such data was that we unexpectedly found correspondence of DTI metrics with those of NODDI in our previous paper. While it is known that these high b-value/multi-shell data take non-Gaussian distribution thus are more appropriate to apply a non-linear model like diffusion kurtosis imaging (DKI)39, there have been also a few reports that high b-value DTI sensitively detect the brain tissue pathologies35. The diffusion data were also fitted to the NODDI model using the optimized value of d// and Accelerated Microstructure Imaging via Convex Optimization (AMICO) 1.040, which re-formulates the original NODDI model as a linear system and shortens the calculation time. The value of d// was optimized for the cerebral cortex (1.1 × 10−3 mm2/s) and changed from the default value (1.7 × 10−3 mm2/s)20, because we are interested in the cerebral cortical gray matter. We used default values of regularization (λ = 0.001 and γ = 0.5) for AMICO.The DTI estimates (FA and MD) were calculated using each dataset of dMRI and the dtifit diffusion tensor modeling tool in Functional Magnetic Resonance Imaging of the Brain Software Library (FSL) 5.09 (ORIG and κ) and the DTI model (FA and MD) were mapped onto the cortical surface, as described previously20. Briefly, the algorithm for surface mapping identifies cortical ribbon voxels within a cylinder orthogonal to the local surface for each mid-thickness surface vertex on the native mesh and weights them using a Gaussian function , which reduces the contribution of voxels that contain substantial partial volumes of CSF or white matter21. The ODI in the original NODDI (ODIORIG) was calculated using the surface metric of κ and equation (5). The maps of DTI-derived NODDI parameters (NDIDTI and ODIDTI) were calculated by converting from DTI maps to NODDI maps based on the mathematical relation . The scripts used in this manuscript are available from NoddiSurfaceMapping (https://github.com/RIKEN-BCIL/NoddiSurfaceMapping).The parameters of the original NODDI model 41. The mean value of each measure for each of the 180 parcels per hemisphere was calculated. NDIORIG and ODIORIG calculated using all the dMRI data were considered ‘a gold standard’ reference. To investigate the linear relationship between DTI-derived NODDI parameters and the original NODDI parameters, the correlations between each parcellated surface map  and the reference in each subject were calculated using Pearson correlation analysis. To investigate whether DTI-derived NODDI parameters are biased, Bland-Altman analysis was performed in each dataset45. Briefly, Bland-Altman analysis is a method to confirm the presence or absence and degree of systematic bias visually by creating a scatter diagram (Bland-Altman plot), which is created by plotting the difference between two pairs of measured values on the y axis and the average value of the two measured values on the x axis.Surface maps of NDI20.Since the quality of the NODDI estimates depends on the image quality and preprocessing, we estimated the practical quality by the temporal signal-to-noise ratio (tSNR) of preprocessed b = 0 volumes and removed 29 surface parcels with tSNR < 17 from the analysis. Therefore, a total of 331 parcels in the whole cortex were used for the analysis. The cutoff was determined empirically in our previous study2) in the datasets (see section 3.1), simulations were performed to clarify whether and how potential sources of error can explain our findings of cortical DTI-derived parameters. One of potential sources is the amount of CSF compartment (Viso) in the voxel, which may be sum of intra-tissue CSF volume and partial voluming of CSF in the subarachnoid space  with an interval of 0.1. Parameters of NODDI was calculated using the simulated three-shell dataset (bAll) and those of DTI and DTI-derived NODDI was calculated using any of seven b-shell datasets .All) in 456 subjects of HCP data were used in the original NODDI, the cortical map of neurite density (NDIORIG) showed high intensity in the primary sensorimotor, visual, auditory cortices as well as the middle temporal (MT) area , cortical distribution of MD showed extremely inversed appearance to NDIORIG     and high b-value one shell (b3000) , but did not strongly correlate when using low b-value one-shell dataset (b1000)   correlation with ODIORIG among all datasets (R = −0.40~−0.62)   Fig. , but not00) Fig. . In corr01) Fig. . As for dataset b000  also showed very similar cortical distributions of NDI and ODI in average surface maps across all subjects , particularly when using high b-value dataset including bAll and b3000. The correlation analysis showed that correlation coefficients between the DTI-derived NODDI and original NODDI parameters were extremely high for both NDI  and ODI   were used, the results of DTI-derived NODDI showed a consistent bias: NDIDTI overestimated by a difference of around 0.20 and ODIDTI by 0.15 to 0.10 as compared with those of original NODDI , both NDIDTI and ODIDTI showed a similar surface distribution to the reference    . The bias of ODIDTI was almost same size as in the three-shell dataset  Fig.  had a co00) Fig.  had alsoset Figs , S5B.1000–2000), NDIDTI was a little different and the correlation coefficient was moderate    did not provide comparable surface maps of NDIDTI , both NDIDTI and ODIDTI showed different surface distributions from the reference  and ODIDTI  . Only ODIDTI showed a similar surface distribution to the reference  Fig. , while ODTI Fig.  and ODIDDTI Fig. . For exaFigs S3, , as wellnce Fig.  and highnce Fig.  and relaORIG and ODIORIG with the reference and they were highly correlated  , while ODIORIG was relatively correlated even though using one-shell datasets   Fig. . However01) Fig. , as showiso) and b-shell dataset. As compared with the reference condition (Viso = 0.1), apparent differences in amount of change in the parameters were found across type of calculation  and b-shell schemes  was reasonably unbiased by altered Viso, the parameters of DTI and DTI-derived NODDI tended to be largely biased particularly used b-shell datasets not including high b-value volumes (b = 3000)   were relatively less biased across ranges of Viso changes. These findings suggest that the error of DTI derived parameters is sensitive to the heterogeneity of CSF partial voluming, particularly when lower b-value data was applied. To confirm the specificity of this findings, we also performed another simulation in which ‘homogeneous’ but small CSF volume fraction was assumed  Fig. . The datin vitro study of optic nerve with q-space analysis of diffusion-weighted spectroscopy31 identified slower-decaying component of dMRI signals which have (1) diffusion displacement restricted to ~2 µm close to axonal diameter, (2) longer T2 than rapid diffusing components including myelin water and (3) dependency on neurite orientation, thus suggesting that this component originated from intra-neurite water. The T2 time for myelin water is very short (10–20 ms), while intra- and extra-cellular water is longer than 60 ms46, thus making dMRI signals with TE = 89.5 ms insensitive to myelin water. The dependency of high b-value diffusion-weighted signal on neurite orientation is largely caused by neurite membrane rather than by other longitudinal structures including myelin and neurofilaments47. Simulation showed that the majority of dMRI signals with b-value less than 1000 s/m−2 represents fast-diffusion components which may originate free water such as in CSF compartment. On the other hand, recent high b-value DTI in clinical studies showed higher sensitivity to neurobiological changes than low b-value — apparent diffusion coefficient from high b-value DTI was sensitive to the contrast between gray/white matter32, ischemic/hypoxic changes in the gray matter33, white matter disintegrity in schizophrenia34 and maturation in juveniles35. Taken together, signals obtained at high b-value are likely sensitive to neurobiological changes including neurites, however, there is not completely established model that quantitates a specific property of neurites. The DTI and NODDI are among the most widely used models: the former is a simplified linear model that accounts for Gaussian process of diffusion motion of water molecule in the tissue, and the latter explicitly models neurite properties in the tissue based on non-linear nature in high b-value data.Accumulating evidence have suggested that high b-value dMRI signal is more sensitive to neurites and neural tissue changes than low b-value dMRI signal. The 25 and orientation dispersion28, respectively. The NODDI ODI is relatively robust against data quality5, and well represented the histology-based neurite orientation dispersion in all the studies28 including a single shell and multi-shell dMRI. It is notable that by applying multi-shell dMRI in the spinal cord specimen of multiple sclerosis. Grussu et al.25 revealed that NDI of NODDI was fairly correlated with histology-based density of neurites as assessed by staining neurofilaments. However, since the intrinsic diffusivity is simplified5 (see section of Limitation), careful attention is needed for potential bias depending on the diffusivity, for example, when mixing analysis across tissue subtypes such as gray and white matter20. Therefore, we estimated specific tissue subtype, cortical gray matter and analyzed the relation of DTI metrics, DTI-derived NODDI metrics to the original NODDI.The NODDI is among the most validated models for its predictability of neurite properties. Accumulated histological evidence indicates that NDI and ODI of brain and spinal cord tissues are reasonably correlated with histology-based neurite density48 and those of FA were overestimated50 compared with those of b = 1000 dMRI data. We’ve also confirmed in the simulation that the biases are constant over a possible range of values between DTI-derived NODDI and original NODDI (data not shown). Therefore, the bias of DTI may be due to the effect of kurtosis of high b-value dMRI data. These findings indicate that DTI parameters in cortical gray matter are highly related to those of NODDI when analyzed using high b-value dMRI data and are not very predictive when used low b-value dMRI. This suggests that analyzing cortical microarchitecture by both DTI and NODDI is redundant and does not surpass usefulness of cortical neurite mapping by either way.Here, we showed in healthy subjects that cortical metrics of DTI and DTI-derived NODDI parameters were highly correlated with those of original NODDI, when used a particular set of b-shell scheme in dMRI. 1) the DTI MD was negatively correlated with NODDI NDI when data included high b-value (b = 3000) (R > 0.9), 2) the DTI FA was partially correlated with NODDI ODI and NDI particularly when used middle to lower ranged b-value (b = 1000–2000) (R > 0.87), 3) both NDI and ODI of DTI-derived NODDI showed high correlation with the original NODDI (R > 0.9) when used dMRI data including high b-value (b = 3000). Simulation analysis suggests that less relation of DTI to NODDI when used low b-value data is due to higher sensitivity to heterogeneity in CSF volume fraction in the intra-tissue and/or partial volume. The HCP data and simulation showed that high b-value dMRI data resulted in a constant numerical bias, i.e. same amount of error over the range of values, potentially due to the bias in the DTI measures coming from non-Gaussian distribution of high b-value dMRI data. Since high b-value dMRI data is often in non-Gaussian distribution, applying linear DTI model for such high b-value dMRI data may result in biases of calculated measures as compared with those used b = 1000 dMRI data. Past literature also notes that when using high b-value dMRI data, the values of MD were underestimatediso due to heterogeneity and partial voluming of CSF  and NODDI with 2-shell of b = 1000 and 2000. Interestingly, both NDI and ODI of the NODDI metrics in the cortical gray matter were more sensitive to discriminate patients from controls than those of DTI, which may support higher sensitivity of NODDI than low b-value DTI to the neuropathological changes in this disease. These are in line with our result that DTI-derived NODDI parameters with low to middle b-value data was not strongly correlated with the high b-value NODDI parameters. Mah et al. also studied NODDI and DTI in early adolescent brain and found that NODDI NDI was more sensitive to age-related changes as compared to DTI MD54. They also showed that subcortical gray matter structures, which may be less affected by partial voluming CSF than cortex, showed high correlation between MD and NDI (R = 0.69–0.88) and between FA and ODI (R = 0.70–0.81). In addition, Batalle et al. analyzed cortical metrics of NODDI and DTI in infant brain and results were complicated55. They applied relatively low-resolution dMRI (2 mm) for small sized brain and found the dissociated pattern of changes in the cortical NDI and MD. As expected, MD and NDI showed inversed pattern across ages but only after gestational age of 38-week. Parallel pattern between MD and NDI was found before age of 38-week, which may be due to errors in partial voluming of CSF due to small sized brain and thin cortex. The partial volume effect of CSF may not be negligible in their results, since DTI data was calculated based on the low b-value dMRI data.The current study gives insights and interpretations into recent studies which applied NODDI and DTI in the same sample. In particular, NODDI and DTI showed different sensitivity to the neurobiological changes of interest, while there is a potential variety of CSF contamination and data sampling. Grussu et al. performed NODDI and DTI using multi-shell dMRI data and found higher sensitivity of NODDI NDI than low b-value DTI MD to histology-based marker of neurodegeneration, tau immunoreactivity56. The in vitro study using spinal cord specimen of multiple sclerosis was scanned with multi-shell dMRI including high b-value. They also showed that MD of DTI was negatively correlated with histology-based neurite density in the same specimen. Our previous study which used multi-shell dMRI data also found a very strong relationship between DTI MD and NODDI NDI and that values of FA was also associated with ODI depending on the MD20.Preclinical studies also showed neurobiological changes by NODDI and DTI. Using Alzheimer’s model of transgenic mice, Colgan 57, whereas the original NODDI is recommended with at least 90 directions5, which means three times higher efficiency. HCP-style scanning with high spatial resolution dMRI with 30 directions does not exceed 3 min. Third, low b-value DTI is not appropriate for cortical mapping and suffer from errors from heterogeneity and partial voluming of CSF. The heterogeneity itself cannot be completely estimated by the currently available resolution of dMRI. A special sequence, such as ‘fluid-attenuated inversed recovery DTI’, can be useful by reducing CSF signals59. Meanwhile, the NODDI is more robust against the errors from CSF partial voluming for cortical mapping of microarchitecture.This study provides important implications for future dMRI studies. First, it is redundant to apply both DTI and NODDI to dMRI data for estimating microstructure in cortical gray matter. As formulated by a mathematical conversion from DTI to NODDI, there is no additional quantitative information available by applying both. When used low b-value dataset, DTI suffers from the errors of CSF heterogeneity as compared with NODDI. Therefore, users can choose either method depending on the dMRI data acquired, and neurobiological significance may not be changed. Second, a high b-value DTI is potentially useful for cortical neurite mapping, particularly in clinical setting. A shorter dMRI scan will be particularly helpful for clinical patients such as patients with Alzheimer’s disease who cannot keep still long time. DTI can be estimated with relatively few directions - at least 6 or in general more than 30 are recommended60. There is an attempt to develop a novel method that explicitly analyzes local complexities of diffusivity24. The method is potentially useful for future application; however, it is technically demanding for scanning, particularly, specific diffusion gradient encoding both in linear tensor and spherical tensor. There is also need for investigations on whether the intrinsic diffusivity is significantly changed in-vivo and how it influences the quantification of neurite properties in the gray matter. In the previous study, we optimized the intrinsic diffusivity for the gray matter (1.1 × 10−3 mm2/s) based on non-linear multiparametric fitting20, which resulted in reasonable findings of correlation between neurite density and myelin contrast as expected from histological evidence62. Second, we did not analyze the relationship between DTI and NODDI in the ‘white matter’ in this article. We estimated that the volume fraction of isotropic diffusion is larger in the white matter (0.21 ± 0.1) than in the gray matter (0.09 ± 0.06) (see also Supplementary Text 63. Despite potential larger fraction of CSF, the white matter is not affected by partial voluming of CSF in the subarachnoid space like in the cortical gray matter. Therefore, as long as the volume fraction of CSF is relatively heterogenous across regions in the white matter, the relationship of NODDI and DTI may be similar to those in the gray matter. This is also supported by the current simulation studies on heterogeneity of CSF volume fraction as shown in section 3.2 and Supplementary Text 39 is more suitable for such non-Gaussian dMRI data than DTI. There is also evidence that parameters of DTI such as FA and MD are comparable with those from DKI64, however, we did not include the comparison of DKI with NODDI in this study because of limited space.One of limitations of this study is that it relies on the relative validity of NODDI over DTI. Recently, there is subject of debate about the eligibility on simplification in the NODDI associated with constrained intrinsic diffusivityFor addressing cortical microarchitecture, conventional DTI with low b-value dataset is not very useful because of contamination with the heterogeneity of CSF, whereas NODDI is robust against these factors. Cortical DTI parameters were closely associated with those of NODDI, particularly using data including high b-value data. DTI-derived NODDI based on high b-value dataset showed remarkably similar cortical distributions with those of NODDI, supporting the previous notion of the mathematical conversion between the DTI and NODDI. Simulation also supported these findings that potential intra-tissue CSF fraction and partial voluming of arachnoid CSF may be causing the error and bias in the cortical maps different from those of original NODDI. Although its similarity, analyzing with high b-value dataset and DTI does not add more information for cortical microarchitecture than NODDI.Supplementary Material


Epidermoid cysts are commonly found in various part of the body, but its occurrence in the oral cavity is relatively rare.Present case highlights the importance of involvement of epidermoid cyst within the tongue in 10-year-old child.The occurrence of epidermoid cyst within the tongue is an extremely rare phenomenon and only few cases have been reported in the literature. Clinically they are painless, slowly growing mass causing difficulty in breathing, eating and swallowing Imaging modalities most importantly; ultrasonography helps in the evaluation of the lesion. However, histopathology remains the mainstay for definitive diagnosis in such conditions.From the present case, it is concluded that such type of cases needs to be reported as it highlights the importance of keeping the rarities in mind along with common lesions while making a diagnosis.Jain M. Epidermoid Cyst of the Tongue—A Rare Case. Int J Clin Pediatr Dent 2019;12(1):80–82. A 10-year-old male child patient reported at the Department of Oral Medicine and Radiology with a chief complaint of swelling on the anterior part of the tongue since 3–4 months. The swelling was small in size and progressed gradually over the time to reach the present size. The patient has no history of pain; however, discomfort was present. This was associated with difficulty in speech and mastication as the swelling increased in size of the cyst. The past medical and dental history was not significant. General physical examination and extraoral examination were unremarkable. Intraoral examination revealed a well-circumscribed, round, firm, non-tender, cystic swelling of about 2.5 × 2.5 cm in size, with a smooth surface . Overlyi1 It is a congenital cyst which is derived from the germinal epithelium. This cyst may also be acquired or secondary due to the insertion of epidermal elements into the dermis posttraumatically or iatrogenically, so it has also been termed as the epidermal inclusion cyst.2 Epidermoid cyst has to be differentiated from the dermoid cyst by the lack of skin adnexa such as hair follicles or sebaceous gland, which is present in the dermoid cyst. These lesions are more frequent in young adults and rarely occur in infants,3 but, in our case, it was present in a child. These cysts are generally solitary lesions.4 Epidermoid cyst has been found throughout the body, out of them, only 1.6% has been found in the oral cavity.5 Out of all oral cystic lesions, the epidermoid cyst represents less than 0.01%,6 but, in the present case, the cyst was seen in the oral cavity. In the oral cavity, they are commonly seen on the floor of the mouth,7 but, in the present case, the cyst was seen within the tongue. Epidermoid cyst present within the tongue is very rare entities. In the tongue region, these lesions may be formed by remnants of the tuberculum impar.8 A review of literature indicates that various cases of epidermoid/dermoid cyst affecting extraorally have been reported but only few cases have been reported affecting the oral cavity. In the oral cavity, only a few cases affecting the floor of the mouth have been reported. Shore reported only four cases of sublingual dermoid cyst in a review of 54,000 cases.9 Taylor et al. in their study found that a total of 541 evident dermoid cysts of the body, in which, 184 (34%) were present in the head and the neck and 35 (6.5%) of these on the floor of the mouth.10 New and Erich in a review of 1,459 cases of the dermoid cyst at Mayoclinic found that only 16% of cases involved the oral cavity and, among them, only one-fourth arose from the floor of the mouth.11 Another study of Taylor et al. found that only 65% of cases of the dermoid cyst were intraoral.12 Till date, only one or two cases of epidermoid cyst affecting the tongue have been reported. Here, we report a rare case of epidermoid cyst affecting the tongue. The clinical feature of the lesion is not characteristic and only consists of cystic swelling. As their size enlarges, functional problems such as speech articulation, mastication, deglutition, and respiration can be expected to occur.8 Ultrasonography at best can be considered as an outstanding imaging modality for accurate localization, detection, and extent of the lesion with surrounding structures. However, histopathology remains the mainstay for definitive diagnosis in such conditions. The treatment includes the complete surgical excision.8 Recurrences are very rare. This case is unique because after histopathology, diagnosis of the epidermoid cyst has been arrived at, which was one of the rarest diagnoses at this location.Epidermoid cyst is very rare nonodontogenic cystic lesions with unknown etiology.via histopathology. From the present case, it is concluded that such type of cases needs to be reported as it highlights the importance of keeping the rarities in mind along with common lesions while making a diagnosis.Epidermoid cysts in the tongue are quite rare and need to be differentially diagnosed from numerous other diseases and conditions of the area. The definitive confirmation and diagnosis of the disease is always affected


Mode of birth has been found to be associated with maternal postnatal adjustment with women who have Caesarean Sections (CS) thought to be at higher risk of emotional distress. However the relationship is complex and studies have demonstrated mixed findings. The aim of this study is to evaluate a model that explores the direct relationship between mode of birth and postnatal maternal adjustment at 3 months and indirect relationships through psychosocial variables.n = 2139).A secondary analysis of a population-based survey conducted in England, UK in 2014. The analysis included primiparous women with singleton babies who provided information about mode of birth , showed partial mediation of the relationship between mode of birth and both Maternal postnatal wellbeing and Satisfaction with care during labour and birth. The strongest mediator was Perceived control and the only variable not to show a significant mediation effect was Health of the infant at 3 months.Birth by unplanned, but not planned, caesarean section was associated with poorer maternal adjustment and instrumental birth was associated with lower maternal satisfaction with labour and birth. These relationships were found to be partially mediated by psychosocial variables. Psychosocial interventions in the perinatal period should be considered to optimise maternal postnatal adjustment.The online version of this article (10.1186/s12905-019-0738-x) contains supplementary material, which is available to authorized users. Physical outcomes have been relatively well documented, however, the relationship between mode of birth and psychosocial outcomes would appear to be complex with studies to date demonstrating mixed findings [An increase in births by Caesarean Section (CS) globally, has highlighted concerns that women who experience CS may have poorer physical and psychosocial adjustment after birth –3. Physifindings , 5.A review by Lobel and DeLuca 2007) found that women who deliver by CS had more negative perceptions of their birth experience, themselves and their infants. They demonstrated poorer parenting behaviour and may be at higher risk of emotional distress in comparison to women who had a vaginal birth. These findings have been borne out in further studies with mode of birth being shown to be associated with postnatal depression –8, post-07 found Historically, many of these studies have been small and based on convenience samples and inconsistent findings may reflect these methodological limitations . HoweverMaternal postnatal adjustment, as a psychosocial outcome in these studies, has been variously defined as self-rated health , satisfaFinally, Carquillat, Boulvain and Guitter (2016) recommend that mode of birth not be considered in isolation when exploring its relationship with maternal postnatal adjustment . A numbeThere is a good theoretical rationale for exploring the role of each of these factors as mediators. Perceived control is thought to influence adjustment to acute stressful events  and the Mode of birth  is associated with postnatal maternal adjustment as measured by three indicators: a composite postnatal wellbeing measure, maternal satisfaction with care during labour and birth, and maternal/infant sense of belonging.Perceived control, maternal expectation, support in labour, how long until the mother held her baby mediate the relationship between mode of birth and the three indicators of postnatal maternal adjustment listed above.The 2014 National Maternity Survey is a population based study conducted in England that provides detailed information on women’s experience of care during pregnancy, labour and birth and after birth . It has This study is a secondary analysis of a population-based survey in England, UK in 2014. Content of the questionnaire can be found in the initial survey report .The survey sample was made up of a random sample of 10,000 women who had their baby in a two-week period at the beginning of January 2014 in England. Women were selected for the survey by the Office for National Statistics (ONS) from birth registrations. Women were excluded if their baby had died or they were aged less than 16.n = 2139).ONS provided information on each woman’s age group, marital status, an area-based measure of deprivation (the Index of Multiple Deprivation (IMD) in quintiles), country of birth, and whether or not she had responded to the questionnaire, for this random sample of 10,000 women. This enabled responders and non-responders to be compared. This analysis included all primiparous women with singleton babies who provided information about mode of birth  a postal questionnaire which could be completed and returned to the National Perinatal Epidemiology Unit (NPEU) in a Freepost envelope 2) to complete the questionnaire online, or 3) to complete the survey by telephone, using a Language line interpreter if needed. The questionnaires, each identifiable only by a unique reference number, were returned by post/online/phone to NPEU and logged. The initial mail out took place in April 2014. A reminder letter was sent out 2 weeks later, a further questionnaire was sent out 4 weeks after the initial mail out and again after 8 weeks, if no response had been received.A 26 page questionnaire was completed which took women through their experience of pregnancy, labour and birth, and postnatal care and allowed them to describe the care they had received. The following items and composite variables were taken from the questionnaire.Women were asked what kind of birth they had and were given the following reply options: “Normal  birth”, “Birth using forceps”, “Birth using vacuum cap on the baby’s head (ventouse)” and “A caesarean (through a cut in the abdomen)”. For caesarean births women were asked if the birth was “Planned and carried out before you went into labour”, “Planned, but carried out after you had gone into labour”, or “The result of an unforeseen problem during your labour”. Due to small group size, forceps and ventouse were combined into ‘Instrumental’, and the two categories of planned caesarean were combined. The numbers in each group can be found in Table A composite score for postnatal wellbeing was made up of the following six health and wellbeing questions that significantly correlated with each other . Higher scores reflect better wellbeing and the maximum score was six.This was a single question which asked: ‘Overall, how satisfied or dissatisfied were you with the maternity care you experienced during your labour and birth’ using a 5 point Likert scale with responses ranging from ‘very satisfied’ through to ‘very dissatisfied’.This was one item which asked ‘When did you feel your baby really belonged to you? The response levels were: ‘During pregnancy’, ‘immediately after birth’, ‘in the first few days’, ‘in the first few weeks’, ‘only recently’ and ‘not quite yet.’ It was scored 5 for ‘During pregnancy’ to 0 for ‘Not quite yet’.This was a composite measure of seven questions related to decision-making, receiving information, and control . Each item response was coded 0 or 1 and the overall score was a sum of the item scores, with higher scores indicating greater perceived control (maximum score = 7).This was made up of three items scored 0 or 1  and had a Cronbach’s alpha of 0.82.This variable was made up of one item: ‘Overall how do you feel your labour and birth went?’ with ‘worse than you expected’, ‘more or less as you expected’ and ‘better than expected’ as response options, scored from 0 to 2.This was an indicator of the time elapsed between the baby’s birth and when the mother first held the baby: ‘Soon after your baby was born, were you able to hold your baby?’ If yes, ‘How soon after birth did you hold you baby?’ This was categorised into 0–2 min, 3–15 min, more than 15 min, and women who did not hold their baby soon after birth, and was coded from 0 to 3 where 0 = did not hold baby soon after birth, 3 = held baby within 2 min.In addition to the above psychosocial mediators, a binary indicator of infant health was also included ‘Does your baby have any health problems now?’ . This is in recognition that ongoing concerns about infant health after birth can impact on maternal adjustment at 3 months .Mode of birth was the categorical explanatory variable ; the mediator variables included Perceived control, Expectations, Holding the baby, Health care professional (HCP) support, and Infant health at 3 months; and Maternal adjustment was the outcome variable. Maternal adjustment was considered in three ways: Maternal postnatal wellbeing at 3 months (Model 1); Maternal satisfaction with care during labour and birth evaluated at 3 months (Model 2); and Maternal/infant sense of belonging (Model 3). Separate analyses were conducted for each measure of maternal adjustment.c; not shown in Fig. a in Fig. c’ in Fig. b in Fig. a and c in Baron and Kenny’s mediation model – and the adjusted effect of potential mediators on maternal adjustment (path b) were statistically significant, the proportion of effect mediated was estimated as the relative change in effect between the unadjusted (c) and adjusted effect of mode of birth (c’) on maternal adjustment . The path coefficients for the comparison group were set to zero. Sobel’s test was used to infer whether the amount of mediation observed was statistically significant [The mediation analysis followed the Baron and Kenny method . First, ent Fig. . Mode ofnificant . For infnificant . We usednificant . All anaCompleted questionnaires were returned by 4571 women. Once undeliverable questionnaires were excluded from the denominator this gave a usable response rate of 47%. There was significant under-representation of women who were young, living in deprived areas, and those born outside the UK . HoweverHypothesis 1: Mode of birth  is associated with postnatal maternal adjustment as measured by three indicators: a composite postnatal wellbeing measure, maternal satisfaction with care during labour and birth, and maternal/infant sense of belonging.Univariable analysis of the relationship between mode of birth and mediators and mode of birth and maternal adjustment outcome variables can be found in Table p-value = 0.0481). Instrumental, planned CS and unplanned CS were associated with lower Maternal postnatal wellbeing scores compared to SVD . The characteristics of the 2139 primiparous women with singleton deliveries for whom mode of birth was reported are shown in Table p < 0.001) with older mothers more likely to deliver surgically and some evidence of association between Index of Multiple Deprivation and Satisfaction with care during labour and birth (p = 0.031), with more deprived women reporting lower satisfaction. Also, age of the baby at the time of questionnaire completion was correlated with greater Maternal postnatal wellbeing (p = 0.025). However, overall there were only weak association of the sociodemographic variables with both mode of birth and maternal adjustment outcome and they were therefore excluded from further analyses.Hypothesis 2: Perceived control, maternal expectation, support in labour, how long until the mother held her baby, and infant health at 3 months mediate the relationship between mode of birth and postnatal maternal adjustment as measured by a composite wellbeing measure and maternal satisfaction with care during labour and birth.The effect of each of the potential mediators on Maternal postnatal wellbeing (Table p < 0.001) and the mediation model can be found in Fig. p < 0.001 and p < 0.001 respectively). There was evidence that Expectations regarding labour and birth mediated the associations between unplanned CS and Maternal postnatal wellbeing , and between both instrumental and unplanned CS and Satisfaction with care during labour and birth . Holding the baby soon after birth significantly mediated the associations between unplanned CS and Maternal postnatal wellbeing , and between both instrumental and unplanned CS and Satisfaction with care during labour and birth . Similarly, HCP support significantly mediated the association between unplanned CS and Maternal postnatal wellbeing , and between both instrumental and unplanned CS and Satisfaction with care during labour and birth . Infant health  was NOT a significant mediator of the association between method of birth and Maternal postnatal wellbeing or Satisfaction with care during labour and birth.The association between sociodemographic variables and mode of birth or outcomes was explored. There was strong evidence of association between maternal age and mode of birth , and minimises socially desirable responses by completing the questionnaire independently from the care provider , 59. TheWomen who have an unplanned birth, particularly unplanned CS, may need additional support to promote postnatal wellbeing and maternal satisfaction with labour and birth. There are many ways that women can be supported, for example, providing information around birth that helps parents with their expectations. Increasing support during labour and birth , informiBirth by unplanned CS is associated with poorer maternal adjustment as measured by a composite Maternal postnatal wellbeing variable. Unplanned caesarean section and instrumental birth are associated with lower maternal satisfaction with labour and birth at 3 months postpartum. The relationship was found to be partially mediated by a number of psychosocial variables. Consideration needs to be given to how to enhance support for women throughout pregnancy, labour and birth and the postpartum to promote a sense of control, a trusting relationship with their HCPs and adequately informed expectations around the time of birth. Psychosocial support interventions to promote maternal adjustment following unplanned birth need to be evaluated to identify an optimal approach to care.Additional file 1:Table S1. a: Mediation by Perceived control of the association between mode of delivery and maternal adjustment. b: Mediation by Expectations of the association between mode of delivery and maternal adjustment. c: Mediation by Holding baby of the association between mode of delivery and maternal adjustment. d: Mediation by HCP support of the association between mode of delivery and maternal adjustment. (DOCX 28 kb)


This study was conducted to identify the determinants of low birth weight among infants born in Amhara Regional State Referral Hospitals of Ethiopia.This study found that mothers who delivered female infants , occurrence of health problems during current pregnancy , absence of antenatal care , lack of iron supplementation , maternal MUAC below 23 cm , and gestational age below 37 completed weeks  were found to be determinants of low birth weight. Low birth weight (LBW) is defined as weight of the new born at birth less than 2500 g . GloballLow birth weight has both long and short-term complications unless early screening and interventions have been made . Some ofThe Ethiopian government has acknowledged the severity of the problem, and currently some measures are taken by the government, Nongovernmental organizations , and professional associations like Ethiopian Pediatric Society , 13. HowAn institution based unmatched case–control study was conducted among women who gave birth in Amhara region Referral Hospitals from March 20 to April 30, 2017. The region has a total of five referral hospitals. Of which, three of them were purposely selected because they have a large number of population in their catchment area . These three hospitals serve for more than 15 million populations in their catchment area.All mothers who gave birth in Amhara Regional State Referral Hospitals were our source population. Mothers who gave live births weighed less than 2500 g were considered as cases and those mothers with live births weighed 2500 g and above were considered as controls. Mothers who had single birth infants were included. However, mothers who had infants with congenital anomaly and with chronic diseases (diabetes mellitus and hypertension) were excluded from the study. In addition, those mothers who were seriously ill during the data collection period, and those who were unable to communicate were excluded from the study.The sample size was determined by using a double population proportion formula by considering the following statistical assumptions: 95% confidence interval (Zα/2 = 1.96), 80% power (Zβ = 0.84), case to control ratio 1:2 (r = 2), the odds ratio to be detected ≥ 2 and the 20% control group were exposed. The final sample size of the study was 453 (151 cases and 302 controls).Data were collected by using a structured interviewer administered questionnaire. The questionnaire was adapted from the Ethiopian Demographic and Health Survey. Trained midwives and nurses working outside the respective hospitals conducted the interviews and anthropometric measurements. The weight of the newborns was measured within 15 min after birth using a balanced scale. The scale was always checked and zeroed before weighing each newborn. Maternal height was measured against a wall height scale to the nearest centimeter. Patient records (charts) were also used to take some important variables like maternal hemoglobin level and co-morbid conditions.Data were entered into Epi-data Version 3.1 and exported to SPSS version 22 for further analysis. Summary statistics (mean or median) for continues variables and percentage and frequency for categorical variables were computed for case and control groups separately. Both bivariable and Multivariable binary logistic regression were fitted for each explanatory variable. Finally, in the multivariable binary logistic regression analysis, adjusted odds ratio (AOR) with 95% CI and p-values were used to identify significant variables. Variables having p-value less than 0.05 were considered as significant determinants of low birth weight.From a total of 453 sample size, 429 mothers-baby pairs (143 cases and 286 controls) were included in the final analysis making a response rate of (94.7%). More than half (55.2%) of mothers in cases and 40% in controls had female infants. The majority of mothers in cases and control group (72 and 84.3% respectively) were married. Regarding educational status, 35% of mothers among cases were unable to read and write, and 33.2% of mothers in the control group were diploma and above  of mothers in the case group and three quarters of mothers in the control group had a mid-upper arm circumference of below 23 cm. The majority of the mothers (83.2% in cases and 87.8% in controls) had no history of abortion . The odds of low birth weight was higher among female neonates as compared to their male counterparts . The odds of low birth weight was also higher among mothers who didn’t attend ANC as compared to mothers who attended ANC follow up in the current pregnancy . The odds of low birth weight was also higher among mothers who did not take iron supplementation as compared to mothers who took iron supplementation during the current pregnancy . Mothers who had MUAC below 23 cm  and gestational age below 37 completed weeks  were found to be risk factors for low birth weight  was found to be a risk factor for LBW. Supportive finding were obtained from studies done in Bale zone, Southeast Ethiopia  and TigrThis study found that infant sex being female, preterm, absence of ANC visits, MUAC less than 23 cm, lack of iron or folic acid supplementation and complication during pregnancy the current pregnancy were found to be significant determinants of low birth weight.Since the majority of cases were referred from other health institutions, variables like pre-pregnancy weight and gestational age of the first ANC visit were difficult to access. Therefore, these variables were not addressed in this study. In addition, important variables like physical activity and exposure of ambient air pollution were not assessed because we adapted the EDHS tool, which had no such components.


Environmental exposure to phthalates and bisphenol A (BPA) may have endocrine disrupting effects that alter length of gestation. We assessed the association between the urinary concentrations of 11 phthalate metabolites and BPA with length of gestation in a cohort of women followed from before conception with daily 1st-morning urinary hormone measures that identified day of implantation.Pre-implantation and post-implantation urinary phthalate metabolites and BPA concentrations were measured in pooled urine samples designed to limit single-measure variability due to the likely episodic nature of these exposures and the short half-life of these compounds. We estimated associations between these exposure biomarkers early in pregnancy with length of gestation from implantation to spontaneous birth. Cox proportional hazards models were used to estimate the hazard of birth among 125 naturally-conceived, singleton live births with censoring for medical interventions that artificially shortened pregnancy.p = 0.01). The HR for the molar sum of the four DEHP metabolites measured showed a similar association . Higher concentrations of mono (3-carboxypropyl) phthalate (MCPP), a non-specific metabolite of several high molecular-weight phthalates, measured post-implantation were associated with increased risk of earlier birth, i.e. shorter length of gestation, HR: 1.59, CI: 1.02, 2.49.Higher concentrations of mono (2-ethyl-5-hydroxyhexyl) phthalate (a metabolite of di (2-ethylhexyl) phthalate (DEHP)) during the pre-implantation window were associated with reduced probability of birth, i.e., longer gestations (hazard ratio (HR): 0.55, 95% CI: 0.35, 0.86; Early gestational exposure to DEHP and possibly other high-molecular weight phthalates, (as reflected by urinary MCPP concentrations) may influence the length of pregnancy. Such effects could have consequences for neonatal and maternal health.The online version of this article (10.1186/s12940-019-0522-2) contains supplementary material, which is available to authorized users. There is widespread exposure to phthalates and bisphenol A (BPA) in the U.S. population –3. PhthaThere is natural variability in the length of human gestation , but pren = 221) and kept daily diaries in which they recorded sexual intercourse and menstrual bleeding. Women were followed for up to 6 months for the occurrence of a clinically-recognized pregnancy. Daily first-morning urine specimens were collected in screw top polypropylene jars without preservative throughout the study. For women with a clinically-recognized pregnancy (n = 151), urine collection continued until 8 weeks after their last menstrual period (LMP). Urine specimens were used to measure reproductive hormones to characterize the events of early pregnancy including day of implantation [The EPS was a prospective cohort study conducted in 1982–1986 to estimate the incidence of early pregnancy loss , 23. Briantation . The stuUrine samples were stored for up to 2 weeks in participants’ freezers before collection by study staff and storage at a central location at − 20 °C. After pilot testing to assess the stability of urinary phthalate metabolites and BPA during the storage period , 25, conUrine specimens were analyzed for phthalate metabolites and BPA at the Centers for Disease Control and Prevention (CDC) using online solid-phase-extraction, high performance liquid chromatography-isotope dilution tandem mass spectrometry , 30. TheDaily urine samples were analyzed for human chorionic gonadotropin (hCG) at the time of the original study (1982–1986) using a highly sensitive immunoradiometric assay with a polyclonal antibody . A sustaLength of gestation was quantified as the number of days from implantation to spontaneous birth, with censoring for early delivery due to medical intervention  . We usedn = 6), based on the hCG level on the first non-missing day [Analyses were performed using SAS version 9.4 . The analysis of gestational length from implantation to birth was limited to singleton live births. We imputed the values for women missing date of implantation  phthalate (DEHP) metabolites , we also examined associations between their sum-based molar concentration (∑DEHP metabolites in nmol/mL) and length of gestation. Pre-implantation and post-implantation early pregnancy measures of the chemical biomarkers of interest were assessed separately to examine two windows of exposure with potentially distinct effects. The pre-implantation window measures represent exposure around the time of fertilization and the post-implantation measures represent exposure during the first few weeks after implantation. Potential covariates for analysis were identified from prior analysis of participant characteristics and gestational length . We consP-values were calculated using a chi-square distribution with 1 degree of freedom where the test statistic was calculated as the mean coefficient estimate across imputations squared divided by the total variance [We used Cox proportional hazards models to estimate the associations between the measured exposure biomarkers and gestational length (time from implantation to spontaneous birth). Gestational time was the primary time scale and exposures were examined as dichotomous (above and below the median) and as tertiles. The tertile analysis did not show evidence of a non-monotonic association, so we present the results from the dichotomized exposure analysis only. In this analysis a hazard ratio greater than 1.0 indicates an increased risk of birth (i.e. shorter gestation) among women with exposure biomarker concentrations above the median. This model accounts for the occurrence of medical interventions before spontaneous labor occurs by censoring these gestations at birth. However, 27 women (22%) were missing data on whether they had such medical interventions. We used multiple imputation methods  to imputvariance . For exp and 4 births were post-term .Our sample included singleton live births to mothers who had not been exposed to DES in utero and had measured urinary BPA and phthalate metabolites, resulting in an analytic sample size of 125 women . This corresponded to an approximate 3 day longer pregnancy among women with higher than median concentrations of MEHHP during the pre-implantation window compared to women with lower concentrations. The HR for the composite measure, ∑DEHP, was 0.67, 95% CI: 0.43, 1.05 (approximate 2 day longer gestation for women with higher than median concentrations) and approached statistical significance (p = 0.08). Higher than median concentrations of MCPP during the post-implantation window were associated with increased risk of earlier birth (shorter gestation by approximately 4 days), HR: 1.59, CI: 1.02, 2.49 (p = 0.04). A non-significant and attenuated estimate in the same direction was also seen for the pre-implantation MCPP concentrations and length of gestation. Results from the remainder of the chemical biomarkers examined had wide confidence intervals overlapping 1.0, suggesting null associations.Hazard ratios (HR) for the occurrence of birth by exposure biomarker concentrations are presented in Fig. s cohort , but becThis cohort of naturally conceiving women provided the opportunity to assess how exposure during very early pregnancy (before the pregnancy is recognized) may influence the sequalae of events that occur throughout pregnancy until birth. In the current analysis, we found that the DEHP metabolites all had hazard ratios less than 1.0 indicating longer gestations for women with higher than median concentrations of the 4 measured metabolites . The largest estimate was for pre-implantation MEHHP and this was also the only statistically significant DEHP-metabolite association. We also observed an elevated HR for post-implantation MCCP, a non-specific metabolite of several high molecular weight phthalates, suggesting increased risk of earlier birth. These results suggest that there may be different effects of phthalate exposure during the pre-implantation window compared with the post-implantation window on length of pregnancy, however overlapping confidence intervals limit our ability to draw conclusions about differences in the associations for each of the time windows. The parent compounds of MEHHP and MCPP are high molecular weight phthalates that are commonly used in food packaging, soft plastics, medications, and cosmetics , which rPhthalates and BPA have the potential to interfere with the hormonal changes that occur in early pregnancy , 39 and Studies that have examined the association between urinary phthalate metabolites and BPA during pregnancy and preterm birth have reported mixed results , 21, 42,n = 6) or post-term (n = 4) birth. However, we were able to assess time to birth among conceptuses that implanted, using implantation-based length of gestation. This time period is less variable than using an LMP-based gestational age [Our study had limitations. The women who participated in the EPS were volunteers with no known fertility problems, the majority of whom were well-educated, white, non-smokers, and all of whom were planning a pregnancy, which may limit the generalizability of our results to more diverse populations. The urinary concentrations of phthalate metabolites measured in the EPS women were higher overall compared with concentrations measured in the U.S. general population during the 1999–2000 NHANES . These donal age , which iOur study had several strengths. First, we have a very precise measure of gestational length, and we also account for data on medical interventions (induced labor and Caesarean delivery without labor) that artificially ended pregnancy early. We incorporated this information into our analysis allowing us to focus on natural length of pregnancy. We were also able to identify the day of implantation based on a highly sensitive assay for hCG , allowinTo our knowledge, this is the first study to examine pre-implantation and post-implantation early pregnancy urinary phthalate metabolite and BPA concentrations in relation to length of gestation in a cohort of naturally conceiving women. The associations of MEHHP with longer gestations and MCPP with shorter gestations raise the possibility that exposure to certain phthalates around the time of conception and during very early pregnancy could affect length of gestation. However, confirmation of these findings is needed, particularly given the small sample and multiple exposure biomarkers examined. Reproductive aged women continue to be exposed to compounds with potential endocrine disrupting properties, and those ongoing exposures warrant further investigation of their effects on pregnancy.Additional file 1:Table S1. Spearman correlations between pre-implantation and post-implantation phthalate metabolites and bisphenol A. Table S2. Associations between pre-implantation and post-implantation early pregnancy biomarker concentrations and implantation-based length of gestation. Figure S1. Diagram of the urine samples pooled to measure phthalate metabolites and bisphenol A around the time of conception (pre-implantation measure). The sample period started the day after the end of menses and ended the day before implantation. Each sample was collected approximately 1 week apart and each woman (n = 125) contributed 3 samples to her pooled sample. Figure S2. Diagram of the urine samples pooled to measure phthalate metabolites and bisphenol A after embryo implantation (post-implantation measure). The sample period started the day after implantation and ended approximately 3 weeks later. Each sample was collected approximately 1 week apart and each woman (n = 121) contributed 3 samples to her pooled sample. Figure S3. Flowchart of participants from the Early Pregnancy Study who were included in the analysis of time from implantation to birth. (DOCX 75 kb)


The most important acquired brain injuries in very and extremely preterm infants born in developed countries are periventricular-intraventricular hemorrhages (PIVH) and diffuse white matter injury dWMI, . This brThe first aim of this review is to link the etiopathogenesis of PIVH and dWMI to the standard of care and its latest insights with respect to prevention and reduction of these complications.The second aim is to focus on repair of the sequelae of PIVH and dWMI. There is increasing evidence that repair of perinatal brain injury with trophic and/or stem cell therapy is currently becoming a realistic and exciting option . We discPIVH has still a high incidence in the developed world: 25–35% of preterm infants born before 30 weeks of gestation or a birth weight less than 1,500 g develop PIVH. PIVH develops from the fragile vascular network of the germinal matrix mostly within the first 3 days after birth with the highest incidence in extremely low birth weight infants being up to 45% . AlthougAlthough multifactorial, the pathogenesis of PIVH and its extension to more severe stages is firmly linked to pulmonary immaturity. This is clinically represented by the idiopathic respiratory distress syndrome (IRDS), and  immaturity of the cerebral vascular bed . IRDS mamaternal corticosteroids during imminent preterm birth have shown to reduce the occurrence of PIVH and is common practice during preterm labor and imminent preterm birth in most high income countries since the late eighties of the last century  cyclo-oxygenase inhibitor which showed a positive effect on PIVH incidence and induced (early) patent ductus arteriosus closure . EspeciaHead position and especially left or right deviation of the head of very and extremely preterm infants may affect venous drainage by partial occlusion of the jugular vein. This can induce a temporary increase in intracranial pressure. It has been postulated that this may contribute to the occurrence of PIVH  and Insulin Growth Factor-1 (IGF-1) and its binding protein 3 (IGF-1-BP3) are increasingly recognized to have neuroprotection and PIVH-reducing properties .rization  and brairization . Followirization . The facrization . Intranarization . A cliniIn summary, antenatal corticosteroids and the introduction of exogenous surfactant substantially reduced the PIVH incidence of the preterm born infant in high income countries. Better and non-invasive ventilation techniques together with exogenous surfactant treatment and stress reduction during patient care had a further reducing effect on PIVH incidence, as did prophylactic indomethacin treatment.Promising future therapies for PIVH prevention and/or reduction of severity are delayed cord clamping and early and adequate treatment with trophic factors such as erythropoietin and IGF-1. However, further research is mandatory here. Extremely preterm born infants (or ELGANs) carry a substantial risk of diffuse white matter injury or abnormal white matter development . In the Several causes of cPVL have been suggested, including hypoxia-ischemia and inflammation. Fetal inflammation has been reported to be common in preterm birth , and ‘punctate white matter lesions’ are more commonly seen in extremely preterm infants  Figure . DiffuseA recent review by our group , summariPreterm white matter can be studied in far more detail using MRI, and longitudinal scans can visualize brain growth, including growth of specific brain regions, cortical folding and white matter development , but ideAntenatal and perinatal strategies are very important in the prevention of dWMI. Magnesium sulphate given antenatally to women at risk of preterm birth substantially reduced the risk of cerebral palsy of the infant  tested the pharmacokinetics of maternal and neonatal N-Acetylcysteine. Interestingly, umbilical cord concentrations frequently exceeded maternal concentrations . Future Delayed umbilical cord clamping has been advised in ‘vigorous’ preterm infants. It is associated with significant neonatal benefits, including improved transitional circulation, better establishment of red blood cell volume, decreased need for blood transfusion, and lower incidence of necrotizing enterocolitis, leading to massive systemic inflammation and subsequent white matter injury, and intraventricular hemorrhage .Reduction of severe IRDS not only reduces IVH (see above), but it may also important in the reduction of severe white matter injury. As through a reduction of severe respiratory illness large fluctuations in oxygen and carbon dioxide levels are avoided, production of reactive oxygen species may be reduced. Furthermore, stabilization of blood pressure reduces major swings in cerebral perfusion.postnatal administration of the corticosteroid dexamethasone has been reported to be associated with cerebral palsy  and of brain function (amplitude EEG [aEEG]), may also play an important preventing role with relation to dWMI.2 levels may contribute to cerebral hypoperfusion and white matter injury  versus placebo. The secondary outcome of MRI at term equivalent age showed less white matter injury in the EPO group compared with the placebo group . A meta-melatonin has antioxidant properties by influencing several pathways, and reduces (neuro-) inflammation. Through reduction of proinflammatory cytokines pro-oligodendrocyte maturation could be preserved. Administration of melatonin to pregnant women with fetal growth restriction or pre-eclampsia is under investigation (NCT02395783 and NCT01695070). Neonatal administration of melatonin has been used in preterm newborns with sepsis, surgical procedures or chronic lung disease . Further studies are needed to explore potential neuroprotective effects of IGF-1 with respect to dWMI.lination . Moreovelination . Human slination . Main foIn summary, Injury to and subnormal development of the periventricular white matter is still very common in extremely preterm born infants. Although improved neonatal intensive care may contribute to improved outcomes, additional strategies to counteract (d) WMI may add to an improved neurodevelopmental outcome.Increasing experimental evidence shows that regeneration of the injured immature brain with stem cell-based therapies is promising and may serve as an effective treatment strategy. Stem cells have an intrinsic potential for self-renewal and can differentiate into several cellular phenotypes . Given tvia several routes: intravenously, intracranially/intrathecally and nasally. The nasal route is non-invasive and seems more effective without loss of MSCs in other organ systems as compared to intravenous administration  . Further.Clinical experience is still scarce. Some investigators consider DCC as a form of autologous cord blood transplantation since the number of nucleated cord cells in the newborn which also contain pluripotent stem cells increase . A recenAlthough MSC transplantation seems very promising, it may be clear that further clinical research is mandatory to proof its efficacy to attenuate the consequences of (severe) PIVH. In particular, optimization of dosing of MSCs, the preferred type of MSCs  and mosTreatment with MSCs in preterm neonates with or at risk for dWMI provides us with an exciting and potentially powerful therapy to reduce or even prevent damage to the vulnerable white matter of the preterm neonate. Experimental studies in which perinatal insults as inflammation and hypoxia-ischemia are used separately or in combination showed us already that the paracrine factors secreted by the MSCs promote oligodendrocyte lineage specification, myelination and maturation . It remaAll authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The study aims to develop new birth weight prediction models for different gestational age stages using 2-dimensional (2D) ultrasound measurements in a Chinese population. 2D ultrasound was examined in pregnant women with normal singleton within 3 days prior to delivery (28–42 weeks’ gestation). A total of 19,310 fetuses were included in the study and randomly split into the training group and the validation group. Gestational age was divided into five stages: 28–30, 31–33, 34–36, 37–39 and 40–42 weeks. Multiple linear regression (MLR), fractional polynomial regression (FPR) and volume-based model (VM) were used to develop birth weight prediction model. New staged prediction models  provided lower systematic errors and random errors than previously published models for each gestational age stage in the training group. The similar results were observed in the validation group. Compared to the previously published models, new staged models had the lowest aggregate systematic error (0.31%) and at least a 19.35% decrease; at least a 4.67% decrease for the root-mean-square error (RMSE). The prediction rates within 5% and 10% of birth weight for new staged models were higher than those for previously published models, which were 54.47% and 85.10%, respectively. New staged birth weight prediction models could improve the accuracy of birth weight estimation for different gestational age stages in a Chinese population. Meanwhile, the estimation of fetal weight is helpful to identify small for gestational age (SGA), large for gestational age (LGA), or macrosomia7. Therefore, a reliable fetal weight estimation is of value for clinicians. Numerous EFW models13 were developed using ultrasound measurements, such as head circumference (HC), abdominal circumference (AC), biparietal diameter (BPD) and femur length (FL). Most of these models were established based on ultrasound measurements of Caucasians population. However, the NICHD Fetal Growth Studies showed that the fetal weight differed significantly with race after 20 weeks14.Estimated fetal weight (EFW) is a clinically important indicator to manage perinatal risk and affect the timing and route of delivery17, 2D ultrasound is still widely used in the clinical practice for sonographer compared to 3D ultrasound. Thus, it is necessary to develop more accurate birth weight prediction models based on 2D ultrasound measurements.Although some researchers developed the EFW models using 2D and 3-dimensional (3D) ultrasound measurements in a Chinese population which were confirmed better than those models based on Caucasians populationst study18, the fetal growth velocity is different depending on gestational age. Moreover, Sotiriadis et al.19 found that the divergence between birth weight and the EFW based on the Hadlock’s formula is greater for earlier gestational ages, and the same finding was confirmed in a similar study using Norwegian birth registry20. It is therefore that the single EFW model may not accurately estimate the fetal weight for different gestational age stages.In addition, the common feature of these published EFW models is that fetal weight was estimated by single model for the whole gestational age. According to the Intergrowth-21We postulate that developing different prediction models for each gestational age stage may improve the accuracy of birth weight prediction in a Chinese population. In this study, we established the staged birth weight prediction models from 28 to 42 weeks using 2D ultrasound measurements in a larger Chinese population, and validated its prediction performance in 28–42 weeks.21), 2790 (14.45%) were large for gestational age , and 1073 (5.56%) were macrosomia . The median gestational age at delivery was 39.43 weeks (ranged from 28 to 42 weeks). Table A total of 20,619 women who had an ultrasound scan within 3 days prior to delivery were included, of which 11 women had a stillbirth, 42 women delivered before 28 weeks or after 42 weeks, and 1256 women lacked complete ultrasound measurements . Ultimately, 19,310 women were analyzed in the study. The clinical and demographic characteristics of women and newborns were shown in Table Of 19,310 subjects, 17,377 cases were randomly assigned to the training group and 1,933 to the validation group. Comparisons of demographic characteristics  between the training group and the validation group were shown in Table p = 0.832; 0.923; 0.918). Compared to the MLR and the FPR models, at least a 67.39%, 1.21 times and 7 times decrease in the systematic errors and a 6.97%, 0.26%, 0.36% decrease in the random errors for the VM model were found in 28–30, 31–33, and 34–36 weeks, respectively. The systematic errors of previously published EFW models were higher than new VM models for 28–30, 31–33 and 34–36 weeks except for Haddock  formula (systematic error: 0.23%)  Fig. . For las.05 Fig. . In addip = 0.989; 0.853; 0.922; 0.116; 0.367)   Fig. . In 31–367) Fig. . Similar67) Fig. . For 37–01) Fig. . The ranp-value all <0.001. The random errors for new staged models were equal or smaller than those for published models, while, RMSE values for new models were lower than those for published models. Hadlock  model and new staged models had the same prediction rate within 1% of birth weight (10.86%), which were better than other published models. The prediction rates within 5% and 10% of birth weight for new models were higher than those for other published models, which were 54.47% and 85.10%, respectively.The aggregate systematic errors, random errors, RMSE and prediction rates within 1%, 5% and 10% of birth weight were calculated for each model in the validation group Table . New staThe accuracy parameters  for each birth weight prediction model for detection of SGA, LGA and macrosomia at birth were shown in Tables New single models were also established by MLR, FPR and VM for all gestational ages based on the training group Table . Table S22. Developing the EFW formula requires as many pregnant women as possible who have a standardized ultrasound scan, and birth weight measurement18. To our knowledge, this study is the first report on birth weight prediction for different gestational age stages (from 28–30 to 40–42 weeks) in a large Chinese population including 19,310 newborns. We formed new staged birth weight prediction models, which were best established by VM, MLR and FPR model for first three gestational stages , 37–39 weeks and 40–42 weeks, respectively.Although many EFW models based on 2D ultrasound measurements have been formed, their accuracy was unsatisfiedp = 0.834), but the random error increased 7.85%  and previously published models Fig. . In 28–385% Fig. . In 31–385% Fig. . In 34–385% Fig. . Further85% Fig. , it suggp-value all <0.05  than previously published models for detection of SGA, LGA and macrosomia at birth. To further illustrate the accuracy of staged models, single models were developed using the same methods. Our results showed that single models presented the higher systematic errors, random errors, RMSE values, and the lower prediction rates within 1%, 5% and 10% of birth weight than staged models. The similar results of accuracy for single models and staged models for detection of SGA, LGA and macrosomia at birth were observed. It suggests that staged models had better performance than single models due to the varying growth velocity in different gestational age stages. Thus, we think that new staged models could improve the accuracy of birth weight estimation.What’s more, it is acceptable if the prediction rate within 10% of birth weight was more than 80%23 compared 11 EFW formulas and concluded that there was no preferred model for estimation of fetal weight due to population differences, maternal factors and measurement methods. To avoid the significant differences for fetal weight with race14, some studies reported the EFW models using ultrasound measurements in a Chinese population. Liao et al.15 established an EFW formula using 1,197 fetal biometrics who were delivered between 37 and 41 weeks. Yang et al.16 formed a new birth weight prediction model, in which 290 Hong Kong pregnant women who were delivered at 37–42 weeks were included. However, the prediction models were established using 2D and 3D ultrasound, and sample of two studies was not large and limited to the late third-trimester fetuses. Woo et al.24 developed an EFW formula with only 125 subjects whose detailed information was not included. It was reported that the prediction errors of Woo’s formula were higher than Hadlock’s formula25. Furthermore, our study showed that Woo’s model had higher systematic error, random error and RMSE value, and lower prediction rates within 1%, 5%, and 10% of birth weight than new staged models  or length (FL) measurements may lead to different weight29. Third, fetal position is a factor that affects the measurement of fetal biometrics, which may be addressed by 3D ultrasound15. The use of 2D and 3D ultrasound measurements in the birth weight prediction will be needed for future study.Melamed et al.30 found that more than 3 days of ultrasound-to-delivery intervals tended to affect the accuracy of EFW. Thus, the selection of population in this study is not bias in some extent. Nevertheless, it is remarkable that the application of new staged birth weight prediction models should be cautious in other population study, because many other countries  pregnant women are not routinely scanned in late pregnancy, but are selected for ultrasonography based on pre-pregnancy risk factors and obstetric complications32. Therefore, further studies should be undertaken to verify the accuracy of our new staged birth weight prediction models in other population studies, and explore the influence of population selection for different ultrasound-to-delivery intervals on birth weight prediction.The subjects in this study were those who had both a delivery and an ultrasound 3 days prior to delivery such that it may cause selection bias in theory. However, with the universal use of sonographic technique in clinical practice, Chinese pregnant women receive regular ultrasound scan during pregnancy, especially prenatal ultrasonography which has become an essential part of prenatal diagnosis. Additionally, Cohen 30; fourth, we proposed the staged birth weight prediction models instead of single model, that is, developing the different prediction models for each gestational age stage. Some studies focused on the accuracy of fetal weight estimation for preterm or term fetuses34. For example, Hadlock’s9 and Warsof’s11 formulas usually underestimate preterm fetal weight, and Shepard’s10 formula was likely to overestimate fetal weight at term. This study showed the direct evidence that new staged prediction models could more accurately estimate birth weight.The strengths of this study are, first, it was a study with a large sample size including 1,9310 Chinese fetuses; second, the population was split into the training group and the validation group to better establish models and validate the prediction performance of models, respectively; third, we recruited the pregnant women who had an ultrasound scan within 3 days prior to delivery to avoid the prediction bias caused by wide ultrasound-to-delivery intervals35. Second, due to the smaller sample size for 28–30 weeks compared to others’, the accuracy of the fetal weight estimation may be affected before 30 weeks.There are several limits in our study. First, there are several types of ultrasound machines in the study, and the difference of ultrasound machines calibration may cause the slightly impact on the variability of the measurementsIn conclusion, compared to the previously published models, new staged prediction models presented the higher accuracy in a Chinese population during 28–42 weeks. It suggests that new staged models could be more accurate than single formula on the birth weight prediction for given gestational age stage.37.This was a retrospective cross-sectional study of all women who had an ultrasound examination within 3 days prior to delivery. The study subjects were from two sites: Wuhan Women and Children Medical Care Center (between May 2012 and June 2015), and Shenzhen Luohu Maternity and Children Health Care Hospital (between January 2011 and December 2015). The data was from healthcare information system of two hospitals. Inclusion and exclusion criteria were used: (1) singleton pregnancy, (2) delivery between 28–42 weeks’ gestation, (3) live birth without any congenital malformation, (4) complete measurements . Gestational age was determined using the self-reported last menstrual period (LMP) if it agreed with the ultrasound estimation within 7 days; otherwise, the ultrasound estimation based on crown-lump length was usedAll participants signed inform consents prior to engaging in any study activities. This study was approved by the ethics committee of Tongji Medical College, Huazhong University of Science and Technology, and Wuhan Women and Children Medical Care Center. All the research procedures were performed in accordance with relevant guidelines and regulations.The 2D ultrasound measurements for all participates included the following biometrics: BPD, HC, AC and FL, which were obtained from the ultrasound images and uploaded electronically to the data management system. The three types of ultrasound machines, an ALOKA SSD-5500SV , a Philips iu22 or HD15 , and a GE Voluson E10 or E8 , were used at two sites. Fetal biometrics consisting of BPD, HC, AC and FL were measured in millimeters during the ultrasound examination. BPD was measured from the outer border of the proximal parietal bone to the inner border of the distal parietal bone (“outer to inner”) at the widest part of the skull. HC was obtained by placing the calipers on the outer border of the skull and using the ellipse facility to follow the outer perimeter of the skull to calculate HC. AC measurements were taken at the outer surface of the skin line, using the ellipse facility. For FL, the calipers were placed at the ends of the ossified diaphysis without including the distal femoral epiphysis if it was visible. Birth weight was measured within 1 hour after birth by experienced obstetric nurses using standardized procedures.To control the quality of fetal ultrasound measurements, ultrasound examinations were performed by experienced and certified sonographers with subspecialty training in ultrasound imaging according to a standard protocol. All the sonographers had their scan evaluated for quality control at the early period of the study.The participates were divided into two groups through employing random sampling method: training group and validation group, which were used to establish birth weight prediction models and validate the accuracy of models, respectively. The Student t test and Pearson Chi-square test was used to examine the clinical and demographic characteristics of two groups, including maternal age, maternal weight, maternal height, parity, gestational age, ultrasound-to-delivery intervals, newborn’s gender, and birth weight.38. To determine growth velocity, the “interval method” proposed by Guihard-Costa40 was used, that is, length of pregnancy was divided into 3-week intervals. Therefore, due to the effect of growth velocity on body weight, we divided gestational age into five stages, 3-week intervals per stage, that is, 28–30, 31–33, 34–36, 37–39 and 40–42 weeks. The multiple linear regression (MLR), fractional polynomial regression (FPR)42 and volume-based model (VM)43 were used to establish new birth weight prediction models for different gestational age stages. In the regression model, we also considered the interactions among BPD, HC, AC and FL. In volume-based model, fetal body weight was calculated by the sum of weight of fetal trunk and head. Based on physical and geometric theory, the volume of trunk was expressed as FL × AC2, thus, the weight of trunk was equal to be proportional to FL × AC2. Similarly, the weight of head was proportional to the volume of head, which was modeled as HC3, BPD3, BPD × HC2 and BPD2 × HC. The models, including the variables, the coefficients and the fractional polynomial powers (only for FPR), were elicited by the backward elimination algorithm.It was reported that the growth velocity of Chinese fetal weight showed an inversed “V” shape, and peaked at 34 weeks; furthermore, a significant difference in fetal weight was found at 28, 30, 32 and 38 weeks compared to the published Caucasian data48: (1) comparing systematic error, random error, root-mean-square error (RMSE) and proportion of prediction within 1%, 5% and 10% of actual birth weight for all prediction models. Systematic error was calculated as the mean of percentage error (PE) which was defined as We used two ways to evaluate the accuracy of new staged birth weight prediction models and 21 previously published EFW formulasAll the statistical analyses were carried out in R statistical software version 3.4.1 and SAS Software version 9.4. The statistical significance was set at an α level of 0.05 with a two-sided test.Supplementary Tables S1–S5