Hypothermia is one factor associated with mortality in newborn ruminants due to the drastic temperature change upon exposure to the extrauterine environment in the first hours after birth. Ruminants are precocial whose mechanisms for generating heat or preventing heat loss involve genetic characteristics, the degree of neurodevelopment at birth and environmental aspects. These elements combine to form a more efficient mechanism than those found in altricial species. Although the degree of neurodevelopment is an important advantage for these species, their greater mobility helps them to search for the udder and consume colostrum after birth. However, anatomical differences such as the distribution of adipose tissue or the presence of type II muscle fibers could lead to the understanding that these species use their energy resources more efficiently for heat production. The introduction of unconventional ruminant species, such as the water buffalo, has led to rethinking other characteristics like the skin thickness or the coat type that could intervene in the thermoregulation capacity of the newborn. Implementing tools to analyze species-specific characteristics that help prevent a critical decline in temperature is deemed a fundamental strategy for avoiding the adverse effects of a compromised thermoregulatory function. Although thermography is a non-invasive method to assess superficial temperature in several non-human animal species, in newborn ruminants there is limited information about its application, making it necessary to discuss the usefulness of this tool. This review aims to analyze the effects of hypothermia in newborn ruminants, their thermoregulation mechanisms that compensate for this condition, and the application of infrared thermography (IRT) to identify cases with hypothermia. Hypothermia in newborn animals is caused by exposure to an ambient temperature significantly lower than that of the intrauterine environment (10–15°C lower than the core temperature) . The anivs. altricial)  , anatomitricial) –17.The physiological consequences and productive impact of hypothermia in newborns are potentially severe, so it is essential to recognize this condition promptly. Infrared thermography (IRT) has been proposed as a complementary, reliable, sensitive technique for assessing body temperature in animals , 18, butBos taurus or Bos indicus) or water buffaloes]. Also, a large surface area relative to mass shows a disadvantage in retain heat , while lactate levels increased between 6 and 42 h postpartum to values of 90 mg/dl in hypoxia group and 23 mg/dl in the control group. In a 28 blue and white Belgian calves' study, Cambier et al. (2 in the bloodstream (control group = 77.2 ± 9.9 mmHg vs. hypoxemic group = 47.9 ± 12.9 mmHg). According to those results, hypoxia caused the organisms to utilize anaerobic metabolic pathways that triggered lactate production, which induced a state of acidosis and hypoglycemia that reduced the animal's capacity for thermogenesis  are negatively related to immunoglobulin G1 absorption (IgG)  . However for 1 h . TherefoLikewise, environmental factors such as the year's season may expose newborn ruminants to low temperatures. For example, for species like the water buffalo, there are reports of high mortality rates in autumn, when cold and rain predominate . HoweverIn ruminants, the degree of development at birth is considered similar within each species, though differences in rates of brain maturation may occur, as has been seen between lambs, which reach the maximum weight of cerebral parenchyma 50 days after birth, and kid goats where this occurs at just 20 days post-birth . StudiesAnother mechanism that helps prevent heat loss in neonatal precocial species is developing the locomotory system that allows newborns to stand up in the first minutes postpartum. Regueiro et al.  reportedIn the first case, involuntary rhythmic contractions of skeletal striated muscle reflect the ratio of muscle fibers. The importance of a specific muscle fiber ratio lies in the degree of resistance and maintenance of the muscle contractions that can be produced. This has been proposed as a difference that may influence the efficiency of shivering thermogenesis, complemented by the individual morphology, percentage of body fat, diet and the degree of cold acclimatization . Recruit2= 3.40 ± 0.23 mM). Regarding the semitendinosus and biceps femoris muscles, the authors reported a lower ratio of type I to type II/IIa fibers than controls, which correlated negatively with epinephrine concentrations (r = −0.64) but positively with insulin levels (r = 0.62). These results confirm that in the neonatal stage, metabolically active muscle tissue contraction participates primarily in thermogenesis since it contains high energy reserves  that are available to generate heat.Because of their thermogenic characteristics, type II muscle fibers are more important for neonates. In Peinado et al.'s study of sheep , the perGlucose is a valuable energy reserve in newborn ruminants because skeletal muscle glucose is less abundant in these species due to the presence of deposits of brown adipose tissue (BAT), in contrast to animals like piglets . DühlmeiThe placenta maintains fetal glucose levels during gestation, but at birth and during lactation newborns rely on glycogen stores in the liver to maintain adequate plasma concentrations . This leHypoglycemia at birth has been reported in 22.5% of goat kids, but figures are significantly higher for the Sahel breed, twins, and newborns with low birth weight . When neAdipose tissue regulates temperature and energy balance through its metabolic and cellular functions. In mammals, this tissue is divided into white  and brown forms  , 58. BATRangifer tarandus L), BAT is located mainly in the perirenal-abdominal region  15 min after birth, the weight of the piglets at birth presented a positive correlation with surface temperature (r = 0.47).Similarly, the availability of energy resources at birth, such as colostrum, may provide the nutrients required to maintain heat production and compensate for low environmental temperatures , 76, 77.Bubalus bubalis) (n = 50), where the relation between birth weight (high birth weight: 48–59 kg vs. low birth weight: 38–47.9 kg) and surface temperature during the first 48 h of life was monitored in six thermal windows . p < 0.0001), except for the lacrimal caruncle. (B) Comparison of the mean temperature at 24 h after parturition in groups with high and low birth weights. At 24 h post-birth, both groups showed statistically significant differences (p < 0.0001) in all thermal windows. The high-weight group had a mean temperature of 0.4–2.5°C above the low-weight group. (C) Comparison of the mean temperature at 48 h after parturition in groups with high and low birth weights. At 48 h post-birth, significant differences (p < 0.0001) were only found in the nostrils and pelvic limb thermal windows, with an average difference of 0.7°C and 1.8°C, respectively. The effect that birth weight exerts on the thermoregulatory capacity of newborns is due to the limited capacity that low-birth-weight calves have , which is found only in the mitochondrial adipocytes , 65, 101Another factor that intervenes in thermogenesis is birth weight since low-weight animals may not have the energy resources necessary for heat production. As Dwyer  concludevia diet-induced thermogenesis and non-shivering thermogenesis  is the first step to initiating every thermal response. The key role of these receptors, particularly TRPA1, TRPM8, and TRPC5 are triggered by cold stimuli and promote the vasomotor changes or contraction of skeletal muscle . HoweverThe concentration of endogenous energy sources in the form of liver is essential because it is the first metabolic pathway to produce heat . HoweverOn the other hand, the behaviors and postural changes that most mammals develop during exposure to cold to avoid heat loss by transfer, such as grouping or approaching warm areas, are another relevant issue in ruminants . These bThe application of IRT as a tool to assess thermal status in mammals has been described by different authors , 18, 128Finally, the potential for utilizing remote tools like IRT means that we must determine the effectiveness of the various thermal windows that have been described for large ruminants , 133. AlTo confront the adverse effects of hypothermia, precocial species, including ruminants, are born with anatomical, physiological, and morphological characteristics that allow them to maintain their body temperatures. For example, one of the primary mechanisms is vasoconstriction of peripheral blood vessels to reduce heat loss through radiation. However, prolonged vasoconstriction can induce cellular hypoxia and impact the outcome of the newborn.On the other hand, neonates have BAT deposits that serve as a substrate to NST to produce heat and prevent a sudden drop in core temperature, especially evaporative loss due to their wet coats at birth. The amount of BAT depends on the species and anatomical site . Moreover, its activation requires energy resources to produce heat by NST. Therefore, they can have consequences, such as inducing anaerobic metabolism that can lead to a state of acidosis. Finally, shivering thermogenesis requires muscular development of type II fibers in metabolically active muscles such as semitendinosus and biceps femoris, and its thermogenesis needs high energy reserves. One of them is glucose, an element that is limited in newborn ruminants and predisposes them to fast hypoglycemia. For this reason, critical factors like birth weight, available energy resources, and prompt colostrum ingestion need to be considered to prevent cases of hypothermia.All these changes in microcirculation or metabolic activation of adipose tissue and their reflection of the superficial temperature of ruminant newborns allow the use of IRT as a valuable tool for recognizing hypothermia or identifying critical temperature drops during the first hours of life. The implementation of IRT on determining thermal windows such as ocular, auricular, flank, limbs, and neck, among others, have been shown to provide crucial information on the thermal status of newborn and the amount of heat radiation. However, additional validation is required to determine which ones are most appropriate for evaluation as a complementary step to installing preventive practices in ruminant neonates. Although, it should be noted that the lack of information on the application of IRT in newborn ruminants is a limitation and future studies may focus on this aspect.All authors contributed to the conceptualization, writing, reading, and approval of the final manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Adolescent births are associated with numerous challenges. While adolescent birth rates have declined across the U.S., disparities persist, and little is known about the extent to which broader declines are seen within Appalachia.The purpose of this study was to examine the extent to which adolescent birth rates have declined across the subregions of Appalachia relative to non-Appalachia.We conducted a retrospective study of adolescent birth rates between 2012 and 2018 using county-level vital records data. Differences were examined across the subregions of Appalachia and among non-Appalachian counties. Multiple regression models were used to examine changes in the rate of decline over time, adjusting for additional covariates of relevance.About 13.4% of all counties in the U.S. are within the Appalachian region. The rate of adolescent births decreased by 12.6 adolescent births per 1,000 females between 2012 and 2018 across the U.S. While all regions experienced declines in the rate of adolescent births, Central Appalachia had the largest reduction in adolescent births , which was also noted in the adjusted models when compared to the counties of non-Appalachia . Rates of adolescent birth were markedly higher in counties considered among the most socially and economically vulnerable.This study demonstrates that the rates of adolescent births vary across the subregions of Appalachia but have declined proportional to rates in non-Appalachia. While adolescent birth rates remain higher in select subregions of Appalachia compared to non-Appalachia, the gap has narrowed considerably. Adolescent births in the U.S. remain an important public health issue. The majority of births to adolescents are either mistimed or unwanted and pose additional educational, economic, and health-related challenges for young mothers relative to their peers.2Adolescent birth rates have steadily declined in the U.S. over the past three decades, reaching a record low of fewer than 18 births per 1,000 females between 15 and 19 years of age in 2018.Despite the continued progress, the rate of adolescent births remains higher in the U.S. than in other industrialized nations, and adolescent birth rates for Hispanic and non-Hispanic black persons remain two times higher than observed rates among non-Hispanic white teens.9Appalachia consists of mostly rural counties that are disproportionately represented among persistent poverty counties, particularly Central Appalachia.14While disparities related to adolescent births have been documented in Appalachia,The purpose of this study is two-fold: examine differences in adolescent birth rates between Appalachian subregions and examine how changes in adolescent birth rates in the subregions of Appalachia compare to what was observed among non-Appalachian counties during the same period. While adolescent birth rates in Appalachia have been historically higher than what was observed outside of Appalachia, the rate of change over time has not been examined. The extent to which observed differences have persisted or narrowed over time relative to national trends remains unknown.A retrospective study of adolescent birth rates between 2012 and 2018 was conducted using county-level vital records data. These years capture the immediate post-Affordable Care Act (ACA) period with favorable reproductive health policies. County-level birth rates from the National Center for Health Statistics (NCHS) were linked with the American Community Survey (ACS) 5-year population estimates from the Census Bureau. Additional county-level variables of interest included Health Professional Shortage Area (HPSA) categories from the Area Health Resource File (AHRF) and an index-based economic classification from the Appalachian Regional Commission (ARC). Data for all U.S. counties were analyzed, which includes 420 Appalachian counties and 2,723 non-Appalachian counties.11Adolescent birth rates were the primary outcome examined. Consistent with previous research,To account for the influence of socioeconomic vulnerabilities that are more common among the counties of Appalachia that may also explain observed differences in adolescent birth rates beyond geography and consistent with previous literature, the ARC county economic status was included in the analysis.10One-way analysis of variance (ANOVA) was utilized to examine differences in the characteristics of the Appalachian subregions by the covariates of interest. Bivariate differences in adolescent birth rates between 2012 and 2018 were examined for each Appalachian subregion using the unadjusted regression model and predicted means of adolescent birth rates for both years (2012 and 2018). The absolute difference between 2012 and 2018 was examined to identify initial variation in adolescent birth rates at baseline and the rate of change for each Appalachian subregion over time, relative to what was observed among non-Appalachian counties. Generalized Estimating Equations (GEE) were used for the multiple linear regression model. GEE provides population-averaged estimates of the adolescent birth rates, which is relevant to estimating reliable adolescent birth rates of each Appalachian subregion with multiple counties.Moreover, GEE accounts for intra-subject (intra-county) correlation given that adolescent birth rates potentially vary for each county over a period of years. A regression-based difference-in-difference approach was taken for the adjusted model. Time was interacted with the categorical measure denoting Appalachia designation  in a single model to examine differences in the rate of change between 2012 and 2018 while adjusting for additional covariates of interest. Adolescent birth rates among non-Appalachian counties at baseline were the reference value. Coefficients (b) reflect population-averaged differences in adolescent birth rates relative to the reference value.Predicted means and 95% confidence intervals were derived from the adjusted model to further examine longitudinal differences in the rate of change between 2012 and 2018. Importantly, the study accounts for the precision of the estimated adolescent birth rates across the Appalachian subregions by including a weighting factor in all analyses. Since the total and sample female populations in each county within each Appalachian subregion varies with potentially smaller counties having higher adolescent birth rates and vice-versa, the weighting factor was estimated as the ratio of the sample population of females aged 15–19 in each county to the total female population in each county. The study was reviewed by the East Tennessee State University (ETSU) Institutional Review Board (IRB) and deemed non-human subjects research. All data management and analyses were performed using Statistical Analysis Software (SAS) version 9.4 .About 13.4% of all counties in the U.S. are within the Appalachian region . StatistAmong the non-Appalachian counties, 21.8% were designated as at risk or distressed, compared to 42.3% in Southern Appalachia, 30.5% in South Central Appalachia, 92.6% in Central Appalachia, 58.7% in North Central Appalachia, and 8.1% in Northern Appalachia (p<0.001). Whole and partly HPSA designated counties represent 87.4% of non-Appalachia compared to 88.0% in Southern Appalachia, 84.2% in South Central Appalachia, 85.9% in Central Appalachia, 89.1% in North Central Appalachia, and 89.5% in Northern Appalachia (p<0.001).On average, white persons make up 91.5% of the female population across the five Appalachian subregions compared to the 83.2% of the female population in non-Appalachia. On average, a higher proportion of adolescents across the Appalachian subregions were enrolled in Medicaid (47.2%), compared to 40.0% across the non-Appalachian counties. The proportion of residents under age 65 without any health insurance was significantly higher in Southern Appalachia (16.8%) compared to 15.1% across non-Appalachian counties, 16.5% in South Central Appalachia, 13.4% in Central Appalachia, 12.8% in North Central Appalachia, and 9.9% in Northern Appalachia.The adolescent birth rate was markedly higher in Central Appalachia and lower in Northern Appalachia at baseline than what was observed among non-Appalachian counties and the other Appalachian subregions . A similWhile all regions experienced declines in the rate of adolescent births, the level of change was not uniform. The rate of adolescent births decreased by 12.5 adolescent births per 1,000 females between 2012 and 2018 in non-Appalachia. Among the subregions of Appalachia reductions in the adolescent birth rate per 1,000 females ranged from 9.8  to 18.5 . Compared to the non-Appalachian region, Central Appalachia had the largest reduction in adolescent birth rates over the seven-year period.When adjusted for select characteristics, longitudinal differences in adolescent birth rates were noted across the Appalachian subregions . NotablyFemales aged 15–19 years of Hispanic ethnicity ; females aged 15–19 in other racial classifications ; the percentage of females under 18 years enrolled in Medicaid ; and the percentage of uninsured residents under age 65  were significantly associated with higher rates of adolescent births. However, the percentage of non-English speaking individuals  was associated with lower adolescent birth rates.The adjusted predicted means of adolescent birth rates by Appalachia subregion from the adjusted regression models are provided in The study finds that adolescent birth rates are higher in most Appalachian subregions relative to what is observed in non-Appalachia. Notably, adolescent birth rates in Central Appalachia were markedly higher than what was observed among non-Appalachian counties at baseline. However, the rate of decline in adolescent birth rates observed among the counties of Central Appalachia over the 7-year period was considerably steeper than what was observed among non-Appalachian counties and other Appalachian subregions. While adolescent birth rates remain higher in Central Appalachia, the gap has narrowed considerably over time.While the overall adolescent birth rate decreased over time, rates remained much higher among counties demonstrating socioeconomic vulnerability. Adolescent birth rates among the most economically distressed counties were markedly higher than what was observed among the more affluent counties. Socioeconomic vulnerability, as reflected in the ARC economic status classification, is an important factor influencing health outcomes and the counties of Appalachia are disproportionately represented among the at risk and distressed communities—particularly Central Appalachia.The study notes that the percentage of uninsured residents below age 65 was associated with an increased rate of adolescent birth. Insurance coverage is important for enabling access to effective contraception and contributes to positive health outcomes.20Increased Medicaid enrollment among females aged less than 19 years was also associated with increased adolescent birth rates. With the exception of Northern Appalachia, Medicaid enrollment among individuals aged 19 years and under was far more common in Appalachian counties than non-Appalachia. While Medicaid enrollment may provide financial access to services, it may also reflect increasing socioeconomic vulnerability within populations. Recent research has noted that Medicaid expansion has improved access to contraception, specifically noting that the use of most effective contraceptive methods increased in expansion states compared to states that did not expand Medicaid.It is worth noting that adolescent birth rates in Northern Appalachia were lower than what was observed among non-Appalachian counties and other Appalachian subregions at baseline. Northern Appalachia also experienced the smallest decline in adolescent births over the 7-year period, which is largely expected given the lower baseline value. Northern Appalachia also had fewer counties among the most economically distressed, fewer counties designated as Whole Health Professional Shortage Areas, lower proportions of populations of uninsured individuals and lower proportions of individuals aged less than 19 years enrolled in Medicaid than what is observed among non-Appalachia counties and the other Appalachian subregions. These findings underscore that Appalachia is not a homogenous population. Considering Appalachia as a whole, while important, may mask important health disparities and variation in observed outcomes within the subregions.This study is not without limitations. Adolescent birth rates have fallen over multiple decades in the U.S. This study examines a shorter time window within this broader context. It is possible that observed changes during the study period differ within a larger context spanning multiple decades or may not represent broader changes over a longer period. The cause of differences in adolescent birth rates between Appalachia and non-Appalachian counties and within the different Appalachian subregions was not examined specifically and remains an active area for future research. Having a better understanding of the extent to which changes in access to preventive clinical services, sexual behaviors, investments in pregnancy prevention, and other contextual factors is needed and would greatly inform future actionable steps to sustain observed improvements. Also, other sources of variation such as nationwide healthcare policies, state-level expansion of Medicaid programs, and regionally tailored healthcare programs were not considered in this study. Examining the longitudinal effects of state and national policies focused on access to reproductive health services and the influence of varying underlying economic vulnerabilities within the subregions of Appalachia is warranted.23Although potential for missing data, misclassification, and changes to data elements remain a salient issue with any secondary data sources, this study uses large, well-established data source to examine an important public health issue. While we were not able to understand the contextual factors contributing to observed differences in this study, we were able to use these data to uncover important variation in adolescent birth rates. The finding that the overall rate of birth to adolescents is decreasing not just nationally, but within the subregions of Appalachia is encouraging. The underlying reasons why the rate of decline in Central Appalachia was steeper, despite less favorable social, economic, and healthcare-access-related characteristics, is not well understood and needs further attention. The study spans a time frame that includes an unprecedented federal investment in adolescent pregnancy and innovative federal partnerships to focus on the issue and scale up evidence-based interventions, following the ACA.This study examined changes in adolescent birth rates over time among the subregions of Appalachia, relative to non-Appalachia. Available evidence indicates that this is the first study that has examined this issue longitudinally within the subregions of Appalachia. Its findings unmask important variation in adolescent birth rates among the subregions of Appalachia. It finds that adolescent birth rates were higher among most Appalachian subregions relative to non-Appalachian counties. While the overall adolescent birth rate was much higher in Central Appalachia at baseline, significant progress has been made in this subregion with notable declines during the study period. This is encouraging for clinical practitioners at the front lines and affirms the impact of the reproductive health services they provide to adolescents. This is also encouraging for public health practitioners involved in health education and promotion in communities in Central Appalachia. The study also found that community deprivation and limited access to healthcare providers and services were associated with increasing rates of adolescent births, and these factors were more common among the counties of Appalachia. The study has important public health and health policy implications. Its findings may support the efforts of practitioners and advocates seeking to expand access to care in their communities. These findings can be also used to further target public health interventions and provide a more nuanced perspective on health outcomes within the Appalachian subregions.What is already known about this topic? While adolescent birth rates have been declining over the last several decades, geographic variations continue to persist, particularly in rural and socioeconomically disadvantaged regions. Adolescent births are an important public health issue as they pose educational, economic, and health-related challenges for teen mothers and their infants.What is added by this report? Available evidence indicates that this is the first study to examine adolescent birth rates longitudinally within the subregions of Appalachia. The findings unmask important variation in adolescent birth rates among the subregions of Appalachia. This study demonstrates that while adolescent birth rates generally remain higher in Appalachia than in non-Appalachian regions, the gaps among U.S. regions have narrowed substantially.What are the implications for future research? The identification of subregional variation in adolescent birth rates in Appalachia provides a more nuanced view of important differences and changes over time. This research further provides evidence for the importance of increasing access to reproductive healthcare services and comprehensive sexual health education in Appalachia and other rural communities. Further understanding the extent to which changes in access to preventive clinical services, sexual behaviors, investments in pregnancy prevention, and other contextual factors contributed to observed changes over a longer duration is needed and would greatly inform future actionable steps to sustain observed improvements.


Conclusions: We developed a nomogram risk model for any type of birth defect in a Chinese population based on important modifying factors in pregnant women. This risk-prediction model could be a tool for clinicians to assess the risk of birth defects and promote health education.Objective: This study aimed to develop a nomogram for the risk assessment of any type of birth defect in offspring using a large birth-defect database in Northwest China. Methods: This study was based on a birth-defect survey, which included 29,204 eligible women who were pregnant between 2010 and 2013 in the Shaanxi province of Northwest China. The participants from central Shaanxi province were assigned to the training group, while the subjects from the south and north of Shaanxi province were assigned to the external validation group. The primary outcome was the occurrence of any type of birth defect in the offspring. A multivariate logistic regression model was used to establish a prediction nomogram, while the discrimination and calibration were evaluated by external validation. Results: The multivariate analyses revealed that household registration, history of miscarriages, family history of birth defects, infection, taking medicine, pesticide exposure, folic acid supplementation, and single/twin pregnancy were significant factors in the occurrence of birth defects. The area under the receiver operating characteristic curve (AUC) in the prediction model was 0.682 (95% CI 0.653 to 0.710) in the training set. The validation set showed moderate discrimination, with an AUC of 0.651 (95% CI 0.614 to 0.689). Additionally, the prediction model had a good calibration (HL χ Birth defects, also known as congenital anomalies or congenital malformations, can be defined as functional or structural abnormalities in a developing fetus. Birth defects are the leading cause of early miscarriage, stillbirth, neonatal death, infant mortality, and long-term disability ,2,3,4. AThe pathogenesis of birth defects is poorly understood . About 2Our team conducted a large population-based birth-defect survey in 2013 in Shaanxi province, Northwest China. This project is a rare study that examines the risk of birth defects. We analyzed the data from this project and identified numerous risk factors that may be associated with birth defects ,17,18,19Our study is based on a large population-based birth-defect survey conducted between August and November 2013 in Shaanxi province, Northwest China. This survey covered the central area of Shaanxi , south of Shaanxi  and north of Shaanxi . The inclusion criteria were as follows: (1) The participants were women who were pregnant between August 2011 and August 2013 and gave birth before survey; (2) the participants were local residents . The exclusion criterion was inability to communicate clearly. The maternal sociodemographic characteristics  and maternal risk exposure  were collected from the survey. The diagnostic information on birth outcomes at local hospitals, time of diagnosis, and the types of birth defects was also collected. We also collected the occurrence of birth defects between delivery and participation in the survey. A unified questionnaire was used to conduct the investigation by trained public-health investigators. A memory-assisting strategy was used to minimize recall bias. For example, the investigator would remind participants of local crops’ pesticide application schedules to complete their pesticide exposure history, and participants were allowed to have family members to help access memories to obtain their long-term exposure history. In this survey, 82.37% of children were followed up for more than 6 months, 56.32% of children were followed up for more than 12 months, 43.69% of children were followed up for more than 18 months, and 27.80% of children were followed up for more than 24 months. A total of 30,027 women were eventually enrolled in the survey. We excluded 823 women with unclear pregnancy outcomes or missing covariates, leaving a total of 29,204 women with clear pregnancy outcomes and complete questionnaire . Age was maternal age of this pregnancy. Family history of birth defects referred to congenital disabilities of immediate relatives of the couple. Infection referred to a common cold or flu with mild viral infection at least one time during the early pregnancy. Taking medicine was defined as taking any drugs, such as antibiotics, anticancer drugs, or hormones, during early pregnancy. Alcohol drinking refers to drinking alcoholic products at least one time during the early pregnancy. Tobacco exposure was defined as active smoking (≥1 cigarette per week for 3 consecutive months) or passive smoking  during early pregnancy. Pesticide exposure was defined as exposure to insecticide, rodenticide, herbicide, or fungicide during the periconceptional period. Industrial exposure was defined as living within 1 km of mines, paper mills, cement factories, power plants, pesticide factories, and fertilizer factories during the periconceptional period. Folic acid supplementation was defined as regularly taking folic acid only or multiple micronutrients (≥400 µg folic acid per day) in early pregnancy for a duration of more than 3 consecutive months.The Human Research Ethics Committee of Xi’an Jiaotong University approved this study (no. 2012008). Written informed consent was obtained from all adult participants. 2 test.To generate nomograms and perform external verification, subjects from the center of Shaanxi province were assigned to the training group, while subjects from the south and north of Shaanxi province were assigned to the external validation group. Categorical variables were described as frequency (percentage) and the differences between groups were compared using the χWe identified the factors associated with birth defects in the training group using univariate logistic regression models. Variables with statistical significance in the univariate analysis were included in the multivariate logistic regression analysis, and the forward stepwise method was used to select the variables included in the final model. A nomogram was constructed based on the multivariate logistic regression analysis results, and the selected variables were incorporated in the nomogram to predict the birth defects. 2 statistics.The model performance was evaluated using the C statistic, equivalent to the receiver operating characteristic curve (ROC) area under the receiver operating characteristic curve (AUC). In addition, the calibration performance (agreement between observed and predicted frequencies of the birth defects) was assessed by Hosmer–Lemeshow (HL) χp < 0.05 indicated that the difference was statistically significant.Statistical analysis was performed using R software . Two-tailed analysis with In this study, 29,204 pregnant women were enrolled, including 15,723 in the development group and 13,481 in the validation group. Among the 29,204 participants, 562 women’s infants showed birth defects, including 326 cases in the development group and 236 in the validation group. Cardiovascular system defect, musculoskeletal system defect ,and eye, ear, face, and neck defect were the top three birth defects, accounting for 32.92%, 17.92%, and 12.46% of all the birth defects, respectively  with a history of miscarriages (39 points), no family history of birth defects (0 points), infection (27 points), pesticide exposure (40 points), taking medicine (76 points), folic acid supplementation (0 points), or single pregnancy (0 points), the cumulative score of the various prediction indicators was 57 +39 + 27 + 40 + 76 = 239, and the corresponding predicted risk of birth defects in her offspring was 0.15 (15%) . 2 statistics was 8.106 (p = 0.323), which revealed that the prediction model had good calibration  and 0.651  , respectibration .A limited number of predictive models for the risk of birth defects have been developed. This study included 29,204 women in Shaanxi, China, which was by far the largest sample used in the development of a risk-prediction nomogram to assess the risk of any type of birth defect in offspring. In our model, the critical predictors of birth defects were household registration, history of miscarriages, family history of birth defects, infection, taking medicine, pesticide exposure, folic acid supplementation, and single/twin pregnancy.According to the World Health Organization’s estimates, the total prevalence rates of birth defects in developed, middle-income, and low-income countries are 47.2, 55.7, and 64.2 per 1000 live births, respectively. The total prevalence rate of birth defects in China is close to that of middle-income countries ,22. PoveOur study found that living in urban areas and folic acid supplementation were associated with a decreased risk of birth defects. Based on a nationwide hospital-based registry (the Chinese Birth Defects Monitoring Network), from 2006 to 2008, Li X et al. found that the prevalence ratio of neural tube defects in rural women was much higher than in urban women  . In a prOther predictive models have been developed for birth defects. Based on epidemiological field data, using logistic regression, Liang Y et al. developed a prediction model that can be used to identify pregnant women who are at high risk of offspring congenital heart defects in Nanchong City, China . Wang JFThe current study has several limitations. Firstly, it was based on a survey database. Both the maternal lifestyle behaviors  and the data on risk-factor exposure during early pregnancy were obtained through the questionnaire, which will have introduced recall bias. Secondly, some birth defects, especially some genetic diseases, may not have been detected due to the relatively short follow-up of some of the newborns in this study. Thirdly, although we used eight significant factors to establish a prediction model for the risk assessment of the birth defects in the offspring, some variables, such as obesity and gestational diabetes mellitus, which are related to birth defects, were not included in the model because the birth-defect database was limited. Furthermore, the dietary data were collected from only a few of the women in the survey database. Therefore, the nutritional factors, except for the folic acid supplementation, were not included in the prediction model. Therefore, further improvements to the prediction model by adding more prognostic factors are needed in future studies. Additionally, although we used samples from different regions in Shaanxi province to validate the model, we still need evidence from another sample for validation. Lastly, our study was based on the Northwest Chinese population, which should be considered in the extrapolation of the prediction model.In summary, we developed and validated a nomogram risk model for any type of birth defect in a Chinese population based on a large birth-defect survey and important modifying factors in pregnant women. This prediction model accurately predicted the birth defects based on the women’s risk-factor exposure in pregnancy. The model can therefore be a potential tool for clinicians to assess the risk of birth defects and promote health education.


Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup. Neuroimaging is a standard tool in the clinical workup of neurodegenerative diseases. Parameterization of imaging data enables the development of objective and reliable biomarkers. A computational analysis of neuroimaging parameters has the potential to provide insights into complex disease mechanisms and might close important gaps in research . As amyoMagnetic resonance imaging (MRI) is part of the diagnostic procedures of ALS to exclude structural lesions and other etiologies of the clinical presentation. Objective and reliable in vivo neuroimaging biomarkers for early and accurate individualized prognosis and assessment of the cerebral status in ALS are still missing. Diffusion-weighted MRI (DWI) and diffusion tensor imaging (DTI) play a key role in this regard, as this MRI modality can reveal alterations in white matter (WM) by measuring the differences in constraints on the water diffusion in the brain . The widIn ALS, extensive regional alterations in diffusion metrics in the brain have been demonstrated ,13. ConsThe in vivo analysis of specific WM neuronal tracts allows for the neuropathological staging pattern to be translated to a DTI staging scheme for patients with ALS via a hypothesis-driven tract-of-interest-based approach . During In the evaluation of new therapeutic approaches in ALS, the current focus is on survival and loss of functionality as endpoints in clinical trials . For exaThis review summarizes the advantages and the potential of machine learning (ML)-based DTI methods for patient diagnosis and monitoring, and the future design of clinical DTI applications to ALS is conceptualized. As univariate neuroimaging methods have been successful at the group level but are of restricted usefulness at the individual level in providing definitive clinically useful biomarkers, ML approaches could also be employed for improving individual differential diagnosis.z-score approaches, allow quantitative interpretation at the level of the individual subject in ALS [The clinical relevance of FA alterations along the CST is limited in the light of a meta-analysis of 30 studies which showed a pooled sensitivity of 65% for differentiating between patients with ALS and healthy persons . Sensitit in ALS , but capt in ALS . Supervit in ALS . In clint in ALS ,25. SVMst in ALS . Due to t in ALS . Overfitt in ALS . A minimt in ALS . The impIn addition to the prediction outcomes, most ML models also determine the importance of the features used, which enables data-driven feature selection. For such an unbiased approach to diagnostic classification in ALS, an SVM was used with all voxels of the FA maps as features (at a sample size of about 20 per group) . AlthougIn addition to diagnostic classifications, there are a few other ML applications on DTI data see .It could be shown by ML model applications to DWI that the WM network can be used as a biomarker to predict the progression rate in ALS patients at the single-subject level . AdditioThe neuroimaging signatures of patients with ALS might be confounded by disease duration or gender differences . To addrOverall, the combination of DTI parameters with parameters from other MRI modalities significantly increases diagnostic sensitivity. Nevertheless, a perfect identification of all patients with ALS in a group mixed with healthy people seems to remain unachievable solely on DTI metrics.Although diagnostic approaches are most important in the use of ML in clinical neuroimaging, the diagnosis of ALS is based on clinical criteria, such as the El Escorial diagnostic criteria  and the For individualized patient stratification in ALS, models predicting disease progression and survival are of specific interest. Similar to a diagnostic approach , a multiInstead of multiparametric MRI analysis, DTI parameters can also be extended by clinical measurements in the feature sets of classification tasks. In categorical survival predictions, such combinations of imaging features with clinical parameters may lead to improvement in the model accuracy compared to predictions solely based on diffusion metrics ,46. A stAs clustering algorithms are frequently applied to the identification of patterns in unlabeled data sets, a multiparametric data set from WM, an oculomotor, and cognitive parameters associated with neuropathological stages of ALS could be analyzed in a data-driven manner without prior inclusion of the patient’s disease status . In thisFluid markers from CSF or plasma are also conceivable combination parameters in comprehensive multimodal models. Neurofilament (NF) concentrations have previously been shown to have prognostic and predictive value  that migFor longitudinal monitoring and clinical trials, reliable markers sensitive to cerebral progression are needed. Although alterations of the FA in the CST could be monitored longitudinally ,17 and cThe scheduling of more than two visits is especially critical to the validity of imaging parameter trend analyses in longitudinal studies with an odd number of visits in total, e.g., one baseline and two follow-up visits . With thA reliable assessment of  longitudinal WM alterations is essential in ALS for a timely determination of progression rate and enhances the stratification of patients for enrolment in clinical trials. Additionally, studies monitoring asymptomatic carriers of ALS-associated gene mutations for possible conversion to symptomatic disease might benefit from optimized study protocols to detect even subclinical cerebral alterations. For the conceptualization of clinical neuroimaging studies, considering sample numbers, measurement noise, and visit scheduling, simulations have proven to be useful ,69. In oIn longitudinal studies, it is necessary to be aware that diffusion metrics are subject to physiological aging effects ,71 and tThe modeling of the complex trajectories of healthy brain aging with ML approaches based on neuroimaging data has been established for clinical questions in recent years ,75. Such1 to 102, deep learning models require several orders of magnitude in sample size more to perform optimally [a priori feature selection based on neuroanatomical considerations. On the other hand, limiting features only to DTI and T1w metrics of specific neuroanatomical regions also appears to restrict the discriminating power of standard ML models, as different ML approaches with similar preselected multiparametric MRI features led to similar accuracies in diagnostic classifications.One major limitation of DTI-based ML approaches in ALS is the limited availability of data sets of patients. Longitudinal studies often experience high drop-out rates due to the rapid and often not predictable clinical disease progression. However, the number of data sets available often leads to a low SFR and is crucial in the choice of a classification model. While well-regularized standard ML models, such as SVMs or RFs, might be on the edge of overfitting given the typical sample size in ALS of around 20 to 200, i.e., an order of magnitude 10In the context of the performance of DTI-based ML models, the physiological inter-subject variability of diffusion metrics should be discussed in addition to the limited sample sizes in ALS. Many lifestyle factors such as smoking , alcoholFor the implementation of ML in ALS research, different perspectives arise, as shown in To advance the power to discriminate patients from controls and thus contribute truly to faster diagnosis in ALS, normative deep learning models should find their way into ALS imaging. Deep learning models can encode meaningful representations of brain function in a data-driven manner and determine whether an unknown data set belongs to a group of healthy controls in normative modeling . To advaDiffusion kurtosis imaging (DKI) is a promising tool to be used as a biomarker in neurodegenerative diseases and, thus, also in ALS . The DKIAn integration of DTI into clinical trials spans two different aspects: stratifying patients into prognostic groups and providing reliable markers sensitive to cerebral progression. DTI-based ML models that predict progression rate and assess phenotype might be appropriate for stratifying patients for clinical trial enrollment. For the establishment of longitudinal DTI biomarkers in clinical trials, the standardization and optimization of study protocols are essential to enhance longitudinal sensitivity and reliability. The use of DTI as a longitudinal monitoring tool could allow objective monitoring of cerebral progression and might be considered to be an endpoint in clinical trials.The current state of research demonstrates the enormous academic and clinical potential of ML models in the development of DTI-based neuroimaging biomarkers in ALS. A more accurate assessment of cerebral changes seems possible with multiparametric MRI feature sets than with DTI metrics alone. To capture the full spectrum of pathological signatures in ALS, combining DTI with other modalities, e.g., neurophysiology, is promising to guide individualized ML-based patient characterizations. As multimodal integration of diffusion-weighted MRI techniques will be an essential element in the development of neuroimaging biomarkers in ALS, future research should focus on establishing standardized protocols for full patient characterization and multicenter transnational collaborations. Large, well-characterized cohorts enable a profound performance of ML algorithms and, therefore, gain new insights into the complex interplay between neuroimaging and other clinical measures to characterize patients with ALS and its phenotypes.


Is intubation at 10 minutes or less after birth associated with a higher incidence of bronchopulmonary dysplasia or death by 36 weeks’ postmenstrual age in nano-preterm  infants?This cohort study of 230 nano-preterm infants found that the incidence of bronchopulmonary dysplasia or death by 36 weeks’ postmenstrual age did not significantly differ between those exposed to noninvasive and invasive respiratory support at birth. Noninvasive respiratory support at birth may be associated with a higher risk of severe intraventricular hemorrhage or death by 36 weeks’ postmenstrual age in nano-preterm infants.These findings suggest that noninvasive respiratory support in the first 10 minutes after birth is feasible in the treatment of nano-preterm infants, although it may be associated with an increased incidence of severe intraventricular hemorrhage or death by 36 weeks’ postmenstrual age. Infants with gestational age between 22 0/7 and 23 6/7 weeks (referred to as nano-preterm infants) are at very high risk of adverse outcomes. Noninvasive respiratory support at birth improves outcomes in infants born at 24 0/7 to 27 6/7 weeks’ gestational age. Evidence is limited on whether similar benefits of non-invasive respiratory support at birth extend to nano-preterm infants.To evaluate the hypothesis that intubation at 10 minutes or earlier after birth is associated with a higher incidence of bronchopulmonary dysplasia (BPD) or death by 36 weeks’ postmenstrual age (PMA) in nano-preterm infants.This observational cohort study included all nano-preterm infants at a level IV neonatal intensive care unit who were delivered from January 1, 2014, to June 30, 2021. Infants receiving palliative or comfort care at birth were excluded.Infants were grouped based on first intubation attempt timing after birth .The primary outcome was the composite outcome of BPD  or death by 36 weeks’ PMA.P = .24). Severe intraventricular hemorrhage or death by 36 weeks’ PMA was lower in the invasive respiratory support at birth group .All 230 consecutively born, eligible nano-preterm infants were included, of whom 88  were in the noninvasive respiratory support at birth group and 142  were in the invasive respiratory support at birth group. The incidence of BPD or death by 36 weeks’ PMA did not differ between the noninvasive and invasive respiratory support groups (83 of 88 [94.3%] in the noninvasive group vs 129 of 142 [90.9%] in the invasive group; adjusted odds ratio, 2.09; 95% CI, 0.60-7.25; This cohort study’s findings suggest that noninvasive respiratory support in the first 10 minutes after birth is feasible but is not associated with a decrease in the risk of BPD or death compared with intubation and early surfactant delivery in nano-preterm infants. This cohort study of nano-preterm infants examines whether early intubation after birth is associated with a higher incidence of bronchopulmonary dysplasia or death. Bronchopulmonary dysplasia (BPD) is one of the leading morbidities in nano-preterm infants, and it is even higher in infants between 22 0/7 and 23 6/7 weeks’ gestational age. Noninvasive respiratory support at birth is now a commonly practiced intervention in infants born between 24 0/7 and 27 6/7 weeks’ gestation age because several large randomized clinical trials, such as the COIN (Continuous Positive Airway Pressure or Intubation at Birth) trial,3 SUPPORT ,4 and the Vermont Oxford Network (VON) trial,5 have shown that noninvasive respiratory support at birth improves respiratory outcomes in nano-preterm infants.Advances in neonatal-perinatal medicine have improved the survival of infants with progressively lower gestational age during the last few decades.Infants born between 22 0/7 and 23 6/7 weeks’ gestational age are a highly specialized niche subgroup within the extremely preterm infant population  for which a proper descriptive term does not exist. Through this article, we propose the term nano-preterm for infants born between the gestational ages of 22 0/7 and 23 6/7 weeks to differentiate and precisely represent this population. Strong evidence is lacking on whether noninvasive respiratory support at birth is beneficial in nano-preterm infants. The current study aims to address this knowledge gap and evaluate the hypothesis that intubation at 10 minutes or earlier after birth is associated with a higher incidence of BPD or death by 36 weeks’ postmenstrual age (PMA) in nano-preterm infants. The intubation after 10 minutes was chosen as a surrogate for an attempted trial of noninvasive respiratory support after birth.STROBE) reporting guideline.6The current study is a retrospective cohort study of actively treated nano-preterm infants born at the University of Alabama at Birmingham from January 1, 2014, to June 31, 2021, and cared for in the level IV neonatal intensive care unit. Infants receiving palliative or comfort care at birth were excluded. Data were abstracted from a research database of data prospectively collected by trained research nurses, and the outcomes were confirmed by reviewing electronic medical records. Race and ethnicity classification was based on self-reported maternal race and ethnicity. Data on sex, race, and ethnicity were collected to compare baseline characteristic distribution between the study groups. Infants were grouped based on first intubation attempt timing after birth . The institutional review board at the University of Alabama at Birmingham approved the study with a waiver of informed consent because of the noninterventional and observational nature of the study. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ,9 severe intraventricular hemorrhage (grade 3 or 4),10 retinopathy of prematurity (stage ≥2),11 and moderate and/or severe neurodevelopmental disability at 22 to 30 months.12 Short-term secondary outcomes were assessed as a composite outcome with death by 36 weeks’ PMA, and long-term outcomes were assessed as a composite outcome with death by 22 to 30 months.Because death is a competing outcome for prematurity-related morbidities, the composite outcome of prematurity-related morbidities or death was used. The primary outcome was the composite outcome of BPD 2, Fisher exact, Wilcoxon rank sum, and 2-tailed, unpaired t tests as appropriate were used to compare characteristics between groups. Multivariable logistic regression analyses were performed with adjustment for previously reported risk factors associated with death and neurodevelopmental disability, including antenatal corticosteroids, birth weight, and gestational age,13 to quantify the adjusted odds ratios (aOR) and the 95% CIs for the association of timing of intubation by the study group on the outcomes. Analysis was performed using Stata statistical software, version 16 (StataCorp LLC) and SAS statistical software, version 9.4 (SAS Institute Inc). A 2-sided P < .05 was considered to be statistically significant.Descriptive analyses  were used to describe baseline characteristics stratified by groups. The χP < .001) and birth weight  were higher in the noninvasive respiratory support at birth group vs the invasive group. Forty-nine of the 88 infants (55.7%) in the noninvasive respiratory support at birth group were later intubated. The timing of intubation was within the first hour after birth for most infants who required intubation in the noninvasive respiratory support at birth group . Of the 230 infants, 204 were born 22 months before June 2021 and were eligible for 22- to 30-month outcome assessment; 192 infants had 22- to 30-month outcomes available, and 12 infants were unavailable for follow-up. The noninvasive respiratory support at birth group had higher gestational age, higher birth weight, and more antenatal corticosteroid exposure  and 142 in the invasive respiratory support at birth group . Gestational age  between the groups. Severe intraventricular hemorrhage or death by 36 weeks’ PMA was lower in the invasive respiratory support at birth group . All other secondary outcomes were not significantly different (The incidence of BPD or death by 36 weeks’ PMA was 94.3% (83 of 88 patients) in the noninvasive respiratory support at birth group and 90.9% (129 of 142 patients) in the invasive respiratory support at birth group. On multivariable logistic regression analyses, no significant difference was found for BPD or death by 36 weeks’ PMA , and gestational age–specific outcome analyses were not reported.To our knowledge, no comparative studies have been performed on the outcomes of noninvasive respiratory support in nano-preterm infants. Trials with infants of more than 24 weeks’ gestational age have identified noninvasive respiratory support as beneficial. The COIN trial,This study has several limitations. The study was a retrospective, single-center study. The baseline differences between the study groups favored the infants in the noninvasive respiratory support at birth group. The cohort design may also favor the noninvasive respiratory support at birth group because the infants did not need endotracheal intubation in the first 10 minutes after birth. The intended treatment for infants intubated in the first 10 minutes might have been different from the study group assignment based on the timing of intubation. Furthermore, most of the outcomes did not differ between groups.The findings of this cohort study suggest that noninvasive respiratory support in the first 10 minutes after birth is feasible but is not associated with a decrease in the risk of BPD or death compared with intubation and early surfactant delivery in nano-preterm infants. An adequately powered trial is needed to confirm the trends for better short-term outcomes in the invasive respiratory support at birth group seen in the current study.


The increasing prevalence of preterm birth, which is a global phenomenon, is attributable to the increased medical indications, artificial gestations, and some socioeconomic factors. This study was conducted to identify whether development and equality indices are associated with the incidence of preterm birth, specifically, spontaneous and elective preterm births.This retrospective observational study comprised an analysis of data on live births from 2019 in Brazil and on socioeconomic indices that were derived from census information in 2017. Data were summarised using absolute and relative frequencies. Spearman’s correlation was used to determine the correlation between socioeconomic factors and the preterm birth rate. Multiple beta regression analysis was performed to determine the best model of socioeconomic covariates and preterm birth rate. The significance level was set at 5%.ρ = 0.4 for the Gini Index and ρ =  − 0.24 for illiteracy. The best fit modelled the spontaneous preterm birth fraction as a negative function of the Human Development Index (HDI). The best-fit model considered the expected elective preterm birth fraction as a positive function of the HDI and as a negative function of the Gini Index, which was used as a precision parameter.In 2019 in Brazil, the preterm birth rate was 11.03%, of which 58% and 42% were spontaneous and elective deliveries, respectively. For all preterm births, Spearman’s correlation varied from We observed a reduction in the fraction of spontaneous preterm births; however, the distribution was not uniform in the territory: higher rates of spontaneous preterm birth were noticed in the north, northeast, and mid-west regions. Thus, areas with lower education levels and inequal income distribution have a higher proportion of spontaneous preterm birth. The fraction of elective preterm birth was positively associated with more advantaged indices of socioeconomic status.The online version contains supplementary material available at 10.1186/s12889-022-14376-2. Preterm birth, which is defined as birth before 37 weeks of gestation, is a global phenomenon of increasing prevalence , which hThe mechanisms that lead to birth before 37 weeks of gestation are unclear and constitute an expanding field; however, three main factors for spontaneous preterm birth have been proposed: social stress and race; infection and inflammation; and genetics . The conMost of the population-based studies that investigated the related factors and the incidence of preterm birth have been conducted in developed countries. In less developed countries, the relevant research tends to be observational and mostly single-centre studies, due to which the results are not representative of the general population .Previous studies in the United States found that area poverty and deprivation, assessed by schooling, housing, occupation, race, and income indicators, are related to a higher risk of preterm birth , 9. FurtNonetheless, a Dutch study showed contradictory results: non-Western pregnant women who lived in neighbourhoods with a lower social index were not at a higher risk for adverse perinatal outcomes than those who lived in higher index locations . MoreoveThis study aimed to contribute to this literature gap, identifying whether less-developed and equal areas of Brazil are associated with a higher incidence of preterm birth, specifically spontaneous and elective preterm births. The different socioeconomic factors associated with spontaneous and elective preterm births are not commonly explored features in contemporaneous literature.This retrospective observational study used nationwide data that were obtained from national database of live births, the Sistema de Informações sobre Nascidos Vivos (SINASC), and the Brazilian census conducted by the Brazilian Institute of Geography and Statistics (IBGE).).Preterm birth rates from 2014 to 2019 were analysed by the Brazilian Federal Unit (FU) and by year   to 0.4 [Gini Index]). Thus, higher the values of these variables were associated with a higher percentage of preterm birth (direct relationship). A correlogram of preterm birth rates is presented in Additional File Spearman's correlation coefficients for the percentage of preterm births with regard to the socioeconomic standards: HDI Education, HDI Longevity, HDI Income, HDI, Gini Index, and life expectancy, were positive (range: ρ =  − 0.03 (access to sanitation) and ρ =  − 0.24 (illiteracy). Thus, the higher the absolute values of the above-mentioned variables, the lower the percentage of preterm births (inverse relationship).However, for the rates of illiteracy, access to water, and access to sanitation, we found negative Spearman’s correlation coefficients, which ranged from The best fit chosen to model the percentage of preterm birth is shown in Table The expected value of the preterm birth rate as a function of the HDI, Gini Index, and access to sanitation is derived bywhere Figure For example, a state with an HDI of 0.8, presents an increment of 35.56% in the expected value of the percentage of preterm births compared to a state with an HDI of 0.7, given that the Gini Index and sanitation were fixed at their mean values of 0.53 and 39.64, respectively. Figure Similar results were observed in all three groups  of the spontaneous preterm birth fraction; therefore, data are presented by aggregating births from 22 to 36 weeks of gestation.ρ =  − 0.41 [access to water rate] to ρ =  − 0.67 [HDI Income]). Thus, higher values of these variables were associated with a lower percentage of spontaneous preterm births (inverse relationship). The correlogram of the spontaneous preterm birth fraction is presented in Additional File Spearman's correlation coefficients between the fraction of spontaneous preterm births out of total preterm births and the socioeconomic metrics HDI Education, HDI Longevity, HDI Income, HDI access to water and sanitation, and life expectancy were negative . Thus, higher values the above-mentioned variables corresponded with a higher fraction of spontaneous preterm births (direct relationship).However, for the Gini Index and the rate of illiteracy, we found positive Spearman correlation coefficients ; therefore, data are presented by aggregating births from 22 to 36 weeks of gestation.Spearman's correlation coefficients of the fraction of elective preterm births out of the total preterm births with regard to the socioeconomic metrics HDI Education, HDI Longevity, HDI Income, HDI access to water and sanitation, and life expectancy are positive (range from ρ = 0.42 [access to water] to 0.64 [HDI income and HDI]). Thus, a higher value of these variables was associated with a higher elective preterm birth fraction (direct relationship). A correlogram of the elective preterm birth fraction is presented in Additional File Nevertheless, for the Gini Index and rate of illiteracy, we obtained negative Spearman correlation coefficients . Thus, the higher the value of this variable, the lower is the percentage of elective preterm births (inverse relationship).The best fit chosen to model the elective preterm birth fraction is shown in Table The expected value of the elective preterm birth fraction as a function of the HDI and the Gini Index is given byin which The relationship between the HDI and the fraction of elective preterm births is direct Fig. . Thus, aTo our knowledge, this is the most comprehensive study conducted in developing countries regarding socioeconomic influence on preterm birth rate. We observed that areas with lower levels of development in Brazil were associated with higher spontaneous preterm birth fraction, whereas areas with higher development status were associated with higher fraction of elective preterm birth.In our study, we found that, in Brazil in 2019, the mean prevalence of preterm births was 11.03% , which aligns with previous estimates from other studies wherein the prevalence varied from 10.2% to 15% , 26. TheWith regard to the national fractions of spontaneous and elective preterm births in 2019, 58% were spontaneous, with higher rates in the northern region and lower rates in the south and southeast regions. This finding represents a reduction in the fraction of spontaneous preterm births that infers an increment in the fraction of elective preterm births, compared with the results of a 2014 study, which found that 64.6% of preterm births were spontaneous . Higher Considering the total rate of preterm birth, we found a direct relationship between the HDI, the Gini Index, and life expectancy, but discerned an inverse relationship between illiteracy, access to water, and sanitation. Our results present a similar paradox to that observed in other Brazilian studies , 32, wheWhen analysing spontaneous preterm births, we found a direct relationship between illiteracy and the Gini Index and an inverse relationship between HDI, life expectancy, access to water, and sanitation. Our results indicate that areas with lower levels of education and less equality in income distribution, which are risk factors otherwise proven to be relevant, have a higher fraction of spontaneous preterm birth , 33. HigFinally, elective preterm birth presented the converse associations of the spontaneous preterm birth analysis. Higher elective preterm birth rates are associated with advances in obstetric procedures and early diagnosis of maternofoetal conditions, all of which occur more often in richer and more developed locations . IncreasThe main strength of our study was the use of national data, which provided a broad and comprehensive outlook of preterm birth in Brazil. Another unique aspect was the approach adopted to determine both spontaneous and elective fractions of preterm births, which has rarely been explored in Brazilian studies. The use of state-of-the-art statistical methods provides an enriched analysis.The study’s limitations emerge primarily from the use of public national systems of information. Despite recent enhancements in the quality of SINASC data, underreporting remains an issue , especiaOur study showed that higher socioeconomic status was associated with a higher fraction of elective preterm birth rate and, complementarily, lower spontaneous preterm birth. Preterm birth rate substantially differs depending on the development status of the FU, as well as its distribution in elective and spontaneous births.The study provides unique and comprehensive continent-sized and population-based data, which can potentially be used as a powerful tool to orient policy decisions aimed at reducing perinatal complications. Orienting our view on preterm birth by two different entities – spontaneous and elective preterm births – allows targeted interventions according to territory socioeconomic parameters aimed at reducing the rate of preterm births and ensuring better outcomes in the morbimortality of infants and mothers.Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.


Candida albicans cell-surface protein Hwp1 functions in adhesion to the host and in biofilm formation. A peptide from the Gln-Pro-rich adhesive domain of Hwp1 was used to raise monoclonal antibody (MAb) 2-E8. MAb 2-E8 specificity for Hwp1 was demonstrated using a hwp1/hwp1 C. albicans isolate and strains that expressed at least one HWP1 allele. Immunofluorescence and atomic force microscopy experiments using MAb 2-E8 confirmed C. albicans germ-tube-specific detection of the Hwp1 protein. MAb 2-E8 also immunolabeled the tips of some Candida dubliniensis germ tubes grown under conditions that maximized HWP1 expression. The phylogeny of HWP1 and closely related genes suggested that the Gln-Pro-rich adhesive domain was unique to C. albicans and C. dubliniensis focusing the utility of MAb 2-E8 on these species. This new reagent can be used to address unanswered questions about Hwp1 and its interactions with other proteins in the context of C. albicans biology and pathogenesis.The  Candida albicans is frequently associated with the human host as a commensal, reductions in host immune defense or microbiota can result in the fungus overgrowing its niche and causing disease. The ability of the fungal cell to adhere to host surfaces is a key feature that promotes the opportunity for subsequent disease. Considerable effort has been dedicated to characterization of C. albicans cell-surface proteins that are involved in adhesive interactions with host cells, as well as interactions with other microbial cells to form specialized structures such as biofilms . Candida tropicalis MYA-3404 and Candida parapsilosis CDC 317 were purchased from the American Type Culture Collection. Strains were stored at −80°C in 38% glycerol and routinely cultured on YPD agar . Cultures were stored at 4°C for no more than one week before a fresh plate was prepared. YPD liquid medium used the recipe above, excluding the Bacto agar.Wild-type  disease , was purf SC5314 , was a g1 with a kappa light chain. Anti-Als1 1-B2 . The peptide was synthesized and purified at the Protein Sciences laboratory of the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign. Methods for inoculation of mice, hybridoma formation, and enzyme-linked immunosorbent assay (ELISA) screening were published previously  and summls1 1-B2 , anti-Alls1 1-B2  were alsC. albicans cells were described by C. albicans strain was inoculated into 10 ml YPD liquid medium in a 50 ml Erlenmeyer flask and incubated for 16 h at 37°C and 200 rpm. Cells were collected by centrifugation, washed twice in sterile water, counted using a hemacytometer and released into pre-warmed RPMI 1640 culture medium with L-glutamine  at a density of 5 x 106 cells/ml. Cultures were incubated at 37°C and 200 rpm shaking for 90 min. Germ tubes were collected by filtration over a 0.22 μm pore-size filter and fixed in 3% paraformaldehyde. Paraformaldehyde fixative was diluted from 16% Electron Microscopy Sciences Paraformaldehyde Aqueous Solution  using Gibco Dulbecco’s Phosphate Buffered Saline without calcium or magnesium .Detailed methods for culturing and immunolabeling C. albicans cells were washed 3 times in DPBS prior to and between each step in the immunolabeling protocol. Cells were resuspended in 15 μl/ml normal goat serum for 15 min at room temperature to block non-specific antibody binding. Cells were incubated in 18 μg/ml Protein G-purified MAb in DPBS for 60 min at 4°C on a rotating mixer and then in 3 μg/ml fluorescein isothiocyanate (FITC)-conjugated goat anti-mouse IgG F(ab′)2 fragment-specific antibody (Jackson ImmunoResearch 115-096-006) under the same conditions. Wet mounts of immunolabeled cells were examined using an Olympus BX50 microscope equipped with the Olympus Fluoview FV1200 confocal system and Melles Griot Argon (488 nm) laser. An UP-PLAN 100X Oil objective with a Nomarski prism was used for all images that were captured with Olympus Fluoview software.Fixed Candida species to assess immunolabeling with anti-Hwp1 MAb 2-E8. Growth in RPMI medium did not necessarily induce germ tube formation in other Candida species. Hwp1 production in C. dubliniensis CD36 was also assessed following growth in conditions demonstrated to increase HWP1 expression, specifically 10% serum in MilliQ water  in MilliQ water. The culture was incubated for 2 h at 37°C and 200 rpm shaking. Cells were harvested by centrifugation, washed twice in DPBS, and fixed in 3% paraformaldehyde for 10 min. Cells were washed with DPBS and immunolabeling completed as described above. After the final wash step, wet mount cells were visualized using a Keyence All-in-One Fluorescence Microscope (Model BZ-X800E) using the auto-focusing, auto-exposure, and optical sectioning options.A similar method was used to grow cells of different iQ water . A singlImmunofluorescence assays with multiple antibodies used the Alexa Fluor™ 594 Protein Labeling Kit (Invitrogen) to label purified anti-Als3 3-A5 and the Alexa Fluor™ 633 Protein Labeling Kit (Invitrogen) to label purified anti-Als1 1-B2 according to the manufacturer’s instructions. The CAI12 YPD yeast starter culture was grown as described above. Cells were washed, counted, and released into pre-warmed RPMI medium for 70 min at 37°C and 200 rpm shaking. Cells were fixed in paraformaldehyde, washed, and allowed to adhere to a standard glass microscope slide. Cells were blocked with normal goat serum as described above, then treated with 18 μg/ml of Protein G-purified anti-Hwp1 in DPBS. Each incubation was conducted in a humidified chamber at 4°C for 1 h. The slide was washed three times with DPBS between each immunolabeling step. The slide was first treated with FITC-conjugated goat anti-mouse IgG as described above, then with Alexa Fluor 594-labeled anti-Als3 and Alexa Fluor 633-labeled anti-Als1 at concentrations of 20 μg/ml. ProLong Gold Antifade Mountant (Thermo Fisher Scientific) and a coverslip were added to the slide and cured for 24 h before viewing microscopically on the Olympus FV1200 Confocal System.C. albicans CAI12 cells were grown in liquid YPD medium overnight at 30°C with 200 rpm shaking. Cells were collected by centrifugation and then resuspended in RPMI 1640 medium at 37°C for 90 min to promote germ tube formation. Germ tubes were washed and resuspended in phosphate-buffered saline (PBS) then deposited by capillarity on polydimethylsiloxane (PDMS) stamps for atomic force microscopy (AFM) observation as described by C. albicans sequences were downloaded from the Candida Genome Database . European Bioinformatics Institute (EMBL-EBI) tools were used for translating nucleotide sequences, sequence alignment, and other general processes . SignalP-6.0 Server . The Candida Gene Order Browser  or derived using AlphaFold Colab . Structural visualization and alignments used the PyMOL Molecular Graphics System, Version 2.5.2 .Protein structural predictions derived using AlphaFold  were dowC. albicans HWP1, RBT1, and HWP2 were identified from online databases as described above. The resulting sequences were examined for the presence of expected structural features such as a secretory signal peptide at the N-terminus of the predicted protein and a glycosylphosphatidylinositol (GPI) anchor addition site at the C-terminal end. Lack of such features suggested that the sequences may have errors that altered a predicted start site or caused a premature stop codon. Other sequences had unknown information (e.g. NNN) in the center of the coding region. To derive complete, accurate sequences for these genes, PCR primers were designed using the PrimerQuest Tool (https://www.idtdna.com/pages/tools/primerquest) and synthesized by Integrated DNA Technologies . Q5 High Fidelity DNA Polymerase (New England Biolabs) was used according to the manufacturer’s instructions to amplify the problematic regions. PCR products were purified using the MultiScreen HTS 96-well Filtration System (Millipore). Sanger sequencing was completed at the Roy J. Carver Biotechnology Center DNA Services Lab . Three sequences from C. orthopsilosis required correction: CORT_0E03570  was described previously , washed, transferred to glass microscope slides, heat fixed and stained with crystal violet. The total number of germ tubes adherent to 50 BECs was counted and expressed as the mean number of germ tubes per BEC.Assays were conducted in duplicate on three separate days. A total of 2 × 10A mixed model analysis of variance was used to assess the differences between treatments. Day, replicate (rep), treatment (trt), and mean number of germ tubes per BEC (gt) were considered in the analysis. Data were analyzed using PROC MIXED in SAS . Separation of means was performed with the LSMEANS option. Program commands included: data BEC; input day trt rep gt @@; proc mixed data = BEC; class day trt; model gt = trt; random day day*trt; lsmeans trt/pdiff.C. albicans cell surface was demonstrated by immunolabeling wild-type C. albicans cells, a hwp1/hwp1 mutant (CAH7-1A1E2), and hwp1/hwp1::HWP1 reintegrant strain , but not strain CAH7-1A1E2 (hwp1/hwp1). Reintegration of a wild-type HWP1 allele in strain CAHR3 restored MAb 2-E8 immunolabeling, consistent with the conclusion that the MAb is specific for Hwp1 in C. albicans cells. Use of anti-Hwp1 MAb 2-E8 for immunogold electron microscopy showed gold particles distributed across the germ tube surface, associated with the flocculant, outermost layer of the cell   detection of ace Hwp1 Figure 3C. albicans. C. albicans HWP1 (C4_03570W_A) is located on Chromosome 4 near two other genes with which it shares sequence similarity: RBT1 (C4_03520C_A) and HWP2 (C4_03510C_A). The Candida Gene Order Browser  to visualize the position of the 13-mer immunogen; AlphaFold Colab was used to predict a structure from the C. dubliniensis Hwp1 sequence . Negative results for C. tropicalis and C. parapsilsosis were consistent with genomic information that suggested lack of an epitope that could be recognized by anti-Hwp1 MAb 2-E8  to distinguish between C. albicans and C. dubliniensis and tested them on approximately 20 isolates of each species. Size of the amplified fragments was consistent within each species with C. dubliniensis HWP1 having a considerably shorter Gln-Pro-repeat-encoding region than C. albicans HWP1. C. albicans HWP1 allele  that lacked copies of sequences encoding the Gln-Pro repeat as well as a portion of the Ser-Thr-rich C-terminal region of the protein. A C. albicans strain that had two HWP1-2 alleles was defective in biofilm formation. Although variant HWP1 alleles have been described, selection seems predominantly for a consistent HWP1 allele size both in C. albicans and C. dubliniensis. Hwp1 adhesive function would require sufficient Gln-Pro copies, as well as an overall protein length to display the region on the cell surface. It seems highly likely that anti-Hwp1 MAb 2-E8 should recognize all C. albicans and C. dubliniensis isolates. Immunofluorescence, immunogold electron microscopy, and AFM techniques used here support the idea that the Gln-Pro region is displayed prominently on the C. albicans cell surface  includes the entire C. albicans proteome; the user-friendly AlphaFold Google Colab  can predict structures from proteins yet to appear in the AlphaFold database. While AlphaFold is viewed as game-changing in its ability to shed light on structural questions, the algorithm did not arrive at the same conclusions for Hwp1 as previous biochemical analysis. AlphaFold predicted Hwp1 to be largely unstructured  but assigned a low or very low per-residue confidence score to the prediction. C. albicans cell wall . It is reasonable to assume a similar Als1 distribution would occur in the rat catheter biofilm model. In cultured cells, Hwp1 is present strongly along the C. albicans germ tube length, similar to Als3  of the heterodimeric a-agglutinin  function (S. cerevisiaea-agglutinin (Aga1) seems prompted by the observation that Hwp1 production and localization during C. albicans mating is specific to the a/a portion of the conjugation bridge . Other data described in this study can be found in online repositories as specified in the article and The anti-Hwp1 2-E8 hybridoma was deposited in the Developmental Studies Hybridoma Bank, University of Iowa  and IDEX Transversality UFTMIP: 2016-106-CIF-D-DRDV .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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To explore the effect of pre-pregnancy body mass index (BMI) on neonatal outcomes among singletons born after frozen embryo transfer (FET).This large retrospective cohort study included 18,683 singleton infants born after FET during the period from Jan 1, 2007 to Dec 31, 2019. The main outcomes were large for gestational age (LGA) and preterm birth. Logistic regression models with generalized estimating equations for clustering by patients to estimate odds ratios of LGA and preterm birth.Overweight was positively associated with LGA overall (adjusted OR 1.78 [95%CI 1.60-1.98]), and this association was consistent across age categories. The underweight was inversely associated with LGA among mothers younger than 35 years , but this association was no significant among mothers 35 years or older. Overweight was positively and significantly associated with preterm birth overall (adjusted OR 1.52 [95%CI 1.30-1.77]) and consistently across age categories. The underweight mothers younger than 30 years had a decreased risk of preterm birth (adjusted OR 0.70 [95%CI 0.51-0.97]), but the underweight was no significantly associated with preterm birth among women aged 30 years of older.The risks of LGA and preterm birth were increased in singletons born to overweight mothers, regardless of the maternal age. Underweight decreased the risk of LGA and preterm birth for younger mothers. These findings are important for providing preconceptional counseling to specifically targeted women at high risk of LGA and preterm birth.The online version contains supplementary material available at 10.1186/s12958-022-00957-8. Since the birth of the first in vitro fertilization (IVF) baby in 1978, more than seven million babies have been born worldwide through assisted reproductive technologies (ART). With the tremendous advancements in the field of ART over the past 40 years, the ultimate goal of ART has changed from achieving successful pregnancy to having a healthy baby , 3. The The gestation age and birth weight are two important indicators evaluating neonatal health. Excessive fetal growth can result in birth weight of large for gestational age , which substantially increases the risk of short-term and long-term morbidities and mortality –7. In thThe extensive literature showed the increased risk of adverse neonatal outcomes in FET singletons comparing with singletons following spontaneous conception –11. ManyPre-pregnancy body mass index (BMI) is a potentially modifiable and preventable lifestyle-related factor related with neonatal outcomes . HoweverWith the increasing use of FET worldwide, clinical study on the association of pre-pregnancy BMI and neonatal outcomes resulting from FET is very important to provide crucial advice to specifically target women at high risk of adverse neonatal outcomes before ART or during pregnancy. The objective of the current study was to assess the effect of pre-pregnancy BMI on adverse neonatal outcomes including LGA and preterm birth among singletons born after FET. Previous studies have reported the association between pre-pregnancy BMI and neonatal outcomes would differ by maternal age. A population-based cohort study including 7,141,630 singletons reported a crossover effect of maternal age (a change from an inverse association to a positive association) for the association between pre-pregnancy obesity and preterm birth . Sever sAll data used in this study were obtained from the ART database at the Department of Assisted Reproduction of the Shanghai Ninth People’s Hospital, affiliated with Jiao tong University, School of Medicine . Cycle-level information on patient characteristics, details of clinical treatment with ART procedure, and pregnancy outcomes and neonatal outcomes resulting from ART were recorded in this database, which was required by the Technical Standard for Human Assisted Reproduction issued by the Chinese Ministry of Health (CMOH) , 23. Qua2), normal weight (18.5-24.9 kg/m2), and overweight (25.0-29.9 kg/m2). In our population, the number of patients conforming to the WHO class I or higher obesity classes (BMI > 30 kg/m2) was small, so we did not analyze the data from these patients with a BMI > 30 kg/m2. The small number of obese women can be explained by the following reasons. The Asian population in general has a lower BMI than that observed for non-Asian populations ) as mothers with overweight (4.24% [n = 90]). For mothers with underweight, 10.09% (n = 216) of singletons were LGA, whereas for mothers with normal weight, the percentage was 17.24% (n = 2486); for mothers with overweight, the proportion of LGA infant was 26.35% (n = 560). The proportion of preterm birth was 5.65% (n = 121), 7.38% (n = 1064), and 10.87% (231) for mothers with underweight, normal weight, or overweight, respectively.The proportion of singletons with SGA, AGA, LGA, and preterm birth across BMI categories stratifying by maternal age was presented in Table Using the normal weight as the reference, overweight was positively associated with LGA overall (adjusted OR 1.78 [95%CI 1.60-1.98]), and this association was consistent across age categories, whereas the underweight was inversely associated with the risk of LGA among the overall mothers (adjusted OR 0.53 95%CI 0.46-0.62]) (Table 6-0.62) (p-interaction< 0.001). Stratification by maternal age and using birth to normal weight mothers as the reference, the underweight mothers younger than 30 years had a decreased risk of preterm birth (adjusted OR 0.70 [95%CI 0.51-0.97]). To further assess the relationship between pre-pregnancy BMI and preterm birth, we evaluated the joint effects of maternal age with pre-pregnancy BMI on the risk of preterm birth , whereas overweight was positively and significantly associated with preterm birth overall (adjusted OR 1.52 [95%CI 1.30-1.77]) and consistently across age categories (Table -0.93), wIn this large observation study of more than eighteen thousand singletons born after frozen embryo transfer, we found that overweight women, irrespective of age, had significantly increased risk of LGA and preterm birth compared with normal weight women. Compared with normal weight women, a significantly decreased risk of LGA was found among underweight women younger than 35 years, and a significantly lower risk of preterm birth was found among underweight women aged less than 30 years. To our knowledge, this is the first study to explore the relation of pre-pregnancy BMI with the LGA or preterm birth, stratified by maternal age, among singletons born after FET.The primary strength of our study is that we focused on women conceived by FET. To our knowledge, this is the first and the largest study to explore the joint association of maternal pre-pregnancy BMI and age with the risk of LGA and preterm birth among FET offspring. Results from this study could enrich existing research on evaluating the effect of pre-pregnancy BMI on neonatal outcomes. The dataset was obtained from the single center, which reduced the potential confounding effects of differences in ART technique and laboratory environment between centers.2 were not included in this study, which limited the generalizability to other populations with BMI > 30 kg/m2.However, several limitations need to be considered in interpreting our findings. First, although we had a large sample size, the number was small for subjects older than 38 years with underweight when we stratified by LGA or preterm birth. Second, we did not explore the association of obesity and preterm or LGA in this study owing to the small numbers of obese subjects. Third, as an observational study, we could not adjust for some potential confounders, such as endometrial preparation protocol, smoking status, a history of preterm delivery, nutrition conditions and educational status. Finally, patients with a BMI > 30 kg/mThe data from Global Burden of Disease Study showed the proportion of overweight has reached 38% globally for women aged 20 years or older . In ChinPreterm birth is related with the high risk of neonatal mortality and morbidity . SurvivoAlthough both developed and developing countries face a growing burden of overweight and obesity, underweight also remains a significant health public concern among women of childbearing age. By contrasting with previous studies finding that pre-pregnancy underweight was not related with LGA, our results suggest the pre-pregnancy underweight was a protective factor against LGA in women younger than 35 years . HoweverExisting evidences on whether lower maternal BMI influences the risk of preterm birth is debated. Han et al. performed a systematic and comprehensive summary of all studies between 1950 and 2009 including 78 studies and 1,025,794 women in developing and developed countries to elucidate the direction and magnitude of the relation between maternal underweight and preterm birth in singleton pregnancies . The resIn this, the first and largest study evaluating the effects of pre-pregnancy BMI and maternal age on risks of LGA and preterm birth for singletons born after FET to our knowledge, to date, we observed an increased risk of LGA and preterm birth in singletons born to overweight mothers, regardless of the maternal age. Underweight decreased the risk of LGA and preterm birth for younger mothers. These findings are important for clinicians who provide pre-conceptional counseling to specifically targeted women at high risk of LGA and preterm birth. Overweight women should be informed of the perinatal risks and take some measures including dietary adjustment, physical exercise, and behavioral intervention, to optimize their BMI before fertility treatment.Additional file 1: Supplementary table 1. Odds ratios (95%CI) for the relationship of pre-pregnancy body mass index with SGA, by maternal age.Additional file 2: Supplementary table 2. Joint association of maternal age and pre-pregnancy body mass index with risk of SGA.