Prematurity is the leading cause of neonatal morbidity and mortality, specifically in low-resource settings. The majority of prematurity can be prevented if early interventions are implemented for high-risk pregnancies. Developing a prognosis risk score for preterm birth based on easily available predictors could support health professionals as a simple clinical tool in their decision-making. Therefore, the study aims to develop and validate a prognosis risk score model for preterm birth among pregnant women who had antenatal care visit at Debre Markos Comprehensive and Specialized Hospital, Ethiopia.A retrospective follow-up study was conducted among a total of 1,132 pregnant women. Client charts were selected using a simple random sampling technique. Data were extracted using structured checklist prepared in the Kobo Toolbox application and exported to STATA version 14 and R version 4.2.2 for data management and analysis. Stepwise backward multivariable analysis was done. A simplified risk prediction model was developed based on a binary logistic model, and the model’s performance was assessed by discrimination power and calibration. The internal validity of the model was evaluated by bootstrapping. Decision Curve Analysis was used to determine the clinical impact of the model.The incidence of preterm birth was 10.9%. The developed risk score model comprised of six predictors that remained in the reduced multivariable logistic regression, including age < 20, late initiation of antenatal care, unplanned pregnancy, recent pregnancy complications, hemoglobin < 11 mg/dl, and multiparty, for a total score of 17. The discriminatory power of the model was 0.931, and the calibration test was p > 0.05. The optimal cut-off for classifying risks as low or high was 4. At this cut point, the sensitivity, specificity and accuracy is 91.0%, 82.1%, and 83.1%, respectively. It was internally validated and has an optimism of 0.003. The model was found to have clinical benefit.The developed risk-score has excellent discrimination performance and clinical benefit. It can be used in the clinical settings by healthcare providers for early detection, timely decision making, and improving care quality.The online version contains supplementary material available at 10.1186/s12884-023-06018-1. Preterm babies are at a higher risk of morbidity and mortality than other segments of the population . AccordiPrematurity is the leading cause of newborn deaths worldwide  and, thePreterm birth risk factors are poorly understood. However, certain factors are known to increase a woman’s risk of having a preterm birth. Previous research found that maternal age, residence, marital status, Middle Upper Arm Circumference (MUAC), maternal weight, gravity, parity, number of antenatal care initiation(ANC) visits, hemoglobin(HBG) < 11 g/dl, pregnancy status, timing of ANC initiation, iron folate taken, inter-birth interval, medical illness, recent pregnancy complication, past bad obstetrics history, and previous mode of delivery were all significant predictors of preterm birth –31.More than three-quarters of preterm babies can be saved with low-cost care , QualityTo develop and validate a prognosis risk score model for preterm birth among pregnant women who had antenatal care visit at Debre Markos Comprehensive and Specialized Hospital, Ethiopia.In this study, an institution-based retrospective follow-up study was conducted at Debre Markos Comprehensive and Specialized Hospital in Ethiopia to develop and validate a prognosis risk score model for preterm birth among pregnant women who had antenatal care visits.PTB (t0 + 1) = f (X1(t0) + X2(t0) + X3(t0) + …).The theoretical design  of the s0 + 1) = f (Age (t0) + Timing of ANC initiation (t0) + Pregnancy status (t0) + Recent Pregnancy complications (t0) + HGB (t0) + Parity (t0)).PTB .PR (preterm birth) = f .PR (Y = 1) = f (βo + β1 age + β2 timing ANC initiation + β3 pregnancy status + β4 + recent pregnancy complications + β5 HGB + β6 parity).PR (Y = 1) = f (X), where Y = 1 means having preterm birth and Y = 0 means not having preterm birth.The study was conducted at Debre Markos Comprehensive and Specialized Hospital from January 1, 2020 to August 30, 2022, and data were extracted from January 3, 2023 to February 1, 2023.The study population was all pregnant women who had ANC visits at Debre Markos Comprehensive and Specialized Hospital from January 1, 2020 to August 30, 2022. All pregnant women who had ANC follow-ups from January 1, 2020 to August 30, 2022, and gave birth at Debre Markos Comprehensive and Specialized Hospital were included. While, pregnant women who had neither certain Last Menstrual Period (LMP) nor early ultrasound (U/S) evidences and twin pregnancy were excluded.Sample size calculation using rule of thumb. As a general rule, the required sample size in the development of a prediction model is at least 10 events per predictor . We cons1,160.N= (n*10)/I, (12*10)/0.114 = 1,053 with 10% missing = 1,160 records (charts) of study participants.The samples were then chosen using simple random sampling with ANC and delivery registration logbooks. During the study period, 3,423 women received antenatal care at DMCSH. The total number of pregnant women who received antenatal care and gave birth at the hospital was 2,690. The computer-generated simple random sampling procedure was used to select Preterm birth (Yes/ No).Age, marital status, MUAC, maternal weight, and place of residence.Gravity, parity, hemoglobin, pregnancy status, timing of ANC initiation, number of ANC visit, the timing of the start of iron folate, and inter-birth interval.UTI, HIV/AIDS, chronic hypertension, diabetes mellitus, and malaria.Antepartum hemorrhage, PROM, PIH, gestational diabetic mellitus, and threatened abortion.Defined as the birth of a neonate at a gestational age greater than 28 weeks but less than 37 weeks . DuratioGA was calculated using a certain Last Menstrual Period (LMP) date and/or an established early pregnancy ultrasound (U/S) date (up to and including 20 completed weeks of gestation). When the LMP and U/S dates were not correlated, the American College of Obstetricians and Gynecologists (ACOG) recommendation required defaulting to U/S for GA assessment .Performance of model to differentiates pregnant women who give and who did not give preterm birth. AUROC is > 0.9 excellent discrimination ability .the agreement between observed proportions of preterm birth and predicted probabilities preterm birth. Well calibration mean calibration plot show over a 45° line and Hosmer–Lemeshow test statistics which is insignificant (p > 0.05) .Pregnant women who start antenatal care visit after 12 weeks of gestation .are tools that combine multiple predictors by assigning relative weights to each predictor to obtain a risk or probability of a condition happening. It is simple to calculate, easily interpretable, and actionable .A data extraction checklist was prepared on the Kobo Toolbox web-based tool for the collection of data from the mother’s medical records. A modification was made after reviewing some patients’ records. The checklist was arranged into socio-demographic characteristics of the pregnant mothers, medical illnesses, past and recent obstetric characteristics, and birth outcomes. The data were collected by four BSc midwives from January 3, 2023 to February 1, 2023.After reviewing the prior literature, taking suggestions from medical experts, and reviewing more than 40 charts of the study participant’s, a data extraction checklist was developed in English for data collection. After reviewing the charts, I added MUAC and maternal weight. Data collectors were trained for two days by the principal investigator before the actual data collection about the Kobo Toolbox and components of the checklist, the sequence, and how to solve potential problems they may face during data collection time. Frequent and timely supervision of data collectors was undertaken. The collected data were checked for completeness and accuracy during data collection by the principal investigator.Data were collected using Kobo Toolbox. The collected data were exported to STATA version 14 and R Software version 4.2.2 for data management and analysis. Missing data were handled by multiple imputation, which predict the missing values by utilizing the existing information and then substituting the missing values with the predicted values to create a complete dataset with regression by assuming missing values at random. A sensitivity analysis was done  guideline is used for developing and reporting prediction model . Tables Logistic regression analysis was used to evaluate which variables are most powerful in predicting preterm birth . A bivar0”, which is the moment of prognostication. It is written in the following way:0 + 1) = f (Age (t0) + Timing of ANC initiation (t0) + Pregnancy status (t0) + Recent Pregnancy complications (t0) + HGB (t0) + Parity (t0)).PTB  of 25 to 30 years, and 43.5% were between the ages of 25 and 29. More than three-quarters of the mothers lived in urban, and nearly all (97.35%) were married (Table Almost two-thirds of the pregnant women were multigravida. About 150 13.25%) of the mothers had recent pregnancy complications, which 54 (36.24%) had APH followed by PROM 52 (34.67%) and PIH 50 (33.33%) , 8 (33.3%) and 8 (33.3%) of the mothers had chronic hypertension, malaria and urinary tract infections, respectively. About 55 4.86%) of the mothers were sero-positive for HIV (Table .86% of tNearly two-thirds (66.4%) of the pregnancies were planned, and only a quarter 25%) of the mothers had an early initiation of an ANC visit during their pregnancy. More than half (54.06%) of the mothers had four or more ANC visits for a recent pregnancy. The majority (73.59%) of the mothers initiated iron and folate supplementation in the second trimester of their pregnancy. About 52 (4.6%) of the mothers had low hemoglobin levels, and about 7.16% of participants were negative for the Rh factor . Demographic, medical, recent antenatal, and pregnancy-related conditions of mothers were considered for the development of a prognosis risk score model for preterm birth. In the bivariable binary logistic regression analysis  using original beta coefficients, which means a model was 93.1% differentiates pregnant women who give and do not give preterm birth  and calibration plot falling along a 45° line Fig.  or insigThe developed model had excellent performance based on a Brier score of 0.0478 and an approach of 0. The perfect prediction model has a 0 Brier score, and near one has poor accuracy, with scores ranging from 0 to 0.25 is a perfect model.The model’s ability to classify pregnant women with preterm and term births was also assessed in terms of the prediction density plot. There was overlapping between non cases labeled red (mothers without preterm birth) and positive cases labeled green (mothers with preterm birth) along the threshold probability. So, our model is not 100% accurate in predicting or identifying the difference between pregnant women who have preterm births and those who do not Fig. .The optimal cutoff point for the predicted probability of the risk of preterm birth was 0.2125, with the sensitivity, specificity, PPV, and NPV were 82.9%, 93.3%, 60.0%, and 97.8%, respectively, using the beta coefficients of the predictor variables. The accuracy was also 92.1%  = 2 *Age (< 20) + 1 *Timing ANC initiation (late) + 2 *Pregnancy status (unplanned) + 5 *Recent pregnancy complication (yes) + 5 *HGB (< 11) + 2 *Parity (multipara).Risk prediction model = -5.68 + 1.62 age + 0.78 timing of ANC initiation + 1.18 pregnancy status + 3.61 recent pregnancy complications + 4.19 HGB + 1.31 parityAll significant regression coefficients in the final reduced model were used for model development using a simplified risk score. The AUROC in the simplified risk score was 92.6% (89.7-95.6%) Fig. , and theThe optimal cutoff point was suggested by the Youden index  and finaDeveloped prediction models using original beta and simplified risk score had similar discrimination ability as well as comparable sensitivity and specificity for their optimal cut-off points. The possible minimum and maximum score was 0 and 17. According to the developed risk score, of all the mothers included in the study 840 (74.2%) were categorized under low-risk group and the proportion of preterm birth were 1.1%. Two hundred ninety two mothers found to be high risk with the percentage of preterm birth of 9.8%  .The developed model (model) has highest net benefit ratio starting from threshold probability > 0.01 compared to not treating all  and treating all regardless of their risk . The model has the highest net benefit ratio, which has more clinical and public health importance. Therefore, primary attention should be given based on the prognosis model was a higher cost-benefit ratio than not treating all or treating to all regardless of the prediction probability Fig. .This study provides evidence that age, time of ANC initiation, pregnancy status, recent pregnancy complications, HGB, and parity can predict preterm birth in pregnant women who had ANC visits. We quantified the model’s predictive performance using readily available maternal characteristics during pregnancy rather than invasive laboratory and imaging modalities to guide early interventions to reduce the incidence of preterm birth and improve neonatal health and survival in resource-strained settings such as Ethiopia. It would be used to stratify pregnant women who are at high and low risk for preterm birth and to provide additional management and take appropriate measures accordingly.The combined maternal characteristics used to predict the risk of preterm birth were age, timing of ANC initiation, pregnancy status, recent pregnancy complications, HGB, and parity. They were significant predictors retained in the final reduced model. This score is built up with predictors, widely available in current practice, simple to investigate, and quite affordable. All of these parameters had already been proven to be predictors of preterm birth in previously published studies.Age < 20 had significant association with the preterm birth the finding supported by studies conducted in East Africa , France Babies born to mothers who seek late initiation of ANC also had a higher risk of being preterm. Other studies have shown that seeking ANC visits later in pregnancy can increase the risk of preterm births , 55. It Mothers with recent pregnancy complications are a significant predictor of experiencing preterm birth. This finding was consistent with different findings reported that having recent pregnancy complications was a higher risk for preterm birth than not having a recent pregnancy complication .Hemoglobin < 11 g/dl (anemia) during recent pregnancy was also another factor for preterm birth. Similarly, findings from the Amhara region , CaliforGrand parity women and multiparity mothers were at greater risk for preterm delivery compared to null parity. This finding is supported by studies done in India, Nairobi, Kenyatta, and Nigeria , 50, 62.The discriminating ability of the developed model for the risk of preterm birth among pregnant women was excellent . The modThe model’s accuracy is consistent with a prospective cohort study in European to predict preterm birth results, shows combination of vaginal fluid fetal fibronectin (quantitative fFN) and clinical risk factors had AUROC (0.89) . But preThe internal validation was done with 2,000 random bootstrap samples with replacements were drawn from the data set with complete data on all predictors. The model’s predictive performance after bootstrapping was excellent , with a The performance of our model was also evaluated in terms of calibration by calibration plot and statistical test. That showed insignificant statistical test by Hosmer-Lemshow  test and well calibrated  , meaningThe simplified risk score developed from the regression model is easier to use in routine clinical and public health practice and had comparable discrimination power and well calibrated. In our prediction score, using 4 as the cutoff point has an acceptable level of specificity (82.1%), sensitivity (91.0%), and accuracy (83.1%) to predict preterm birth. Depending on the aim of the program and the availability of resources, it is also possible to change the cutoff point to increase either of the accuracy measures. A prospective cohort study was conducted in India to develop an antenatal risk scoring system/scale for the prediction of preterm birth. The study stated that the developed scoring system resulted in good sensitivity at a cutoff point of ≥ 8 of 75.5% . AnotherThe developed prognosis risk score results in an AUC of 0.926, which is excellent accuracy according to the diagnostic accuracy classification. The prediction accuracy to differentiate pregnant mothers who had a high and low risk of preterm birth is 92.6%, which is determined by the predictors. It is useful to know what proportion of women who go on to experience preterm birth are identified by risk scoring as being at high risk (sensitivity of the test) and what proportion of those who do not go on to experience preterm birth are identified by the test as being at low risk (specificity).We used decision curve analysis (DCA) to assess the model’s clinical and public health impact. Despite the fact that model performance was evaluated in terms of discrimination and calibration, it was satisfactory. In a wide range of threshold probabilities, the decision curve analysis revealed that the developed risk score for early categorization of preterm birth risk has greater clinical benefit than treating none or treating all pregnant mothers. The decision curve analysis concept is standard net benefit along threshold probability. The standard net benefit is calculated by subtracting the benefit from the cost. In the case of this study, the benefit is treating true positives (after correctly predicting pregnant women will have preterm birth), while the cost is treating false positives (incorrectly predicting pregnant women will experience preterm birth).The net benefit of the model-based treatment decision is greater than the net benefit of treating none (assume no women have preterm birth) or treating all . The developed model is compared to two extreme scenarios: “treating all” women results in unnecessary interventions for those at low risk or involves the expenditure of time, effort, and money, and " treating none " results in a lack of intervention for those high-risk mothers, leading to increased prematurity and its associated complications regardless of risk. Our model assumes that pregnant women who are at higher risk of preterm birth are treated by providing high-level antenatal care aimed at preventing preterm birth in those identified as being at increased risk, corticosteroid administration, antibiotic treatment in the event of infection, or by transferring them in utero to a hospital with neonatal intensive care available.It was conducted with an adequate sample size (adequate number of participant outcomes) for predicting preterm birth, which helped construct the model using a sufficient number of candidate predictors and protect overfitting. The model was developed based on easily accessible and measurable maternal characteristics that can be applied in any clinical setup. Moreover, it was validated internally, and the optimism coefficient was minimal, indicating that it is less likely that the model is sample-dependent.The study was not without limitations, in retrospectively collected data, some variables for the prediction of preterm birth might have been missed. However, models developed using retrospectively collected data are still important in resource-limited settings like Ethiopia. However, internal validation was done, It would have been better if it had gone through external validation to ensure its prediction capability when applied to other contexts.The developed risk score model is easy for clinical application, including predictors that can be easily accessed with no advanced clinical assessment of pregnant women. The prediction score will help with the risk stratification of pregnant women and identify those at higher risk of preterm birth. Subsequently, high-risk groups can be given special attention and care, or we can early identify women who are at higher risk of premature delivery so that we can offer prophylactic interventions and guide antenatal management decisions. The women’s categorization into low and high-risk groups provides optimized sensitivity and specificity. Clinicians are believed to be better informed by calculating the risk estimate using an appropriate cut-off for each pregnant woman and making decisions based on conditions.Our prediction model includes variables that are easily obtained and have a high enough accuracy to be used by both mid-level and lower-level health professionals in primary care settings. Five of the maternal characteristics included in our model can be easily identified through history-taking and one through hemoglobin testing. As a result, this feasible prognosis risk score would provide a chance to reduce neonatal complications associated with prematurity, thereby improving overall maternal and child healthcare. The model is critical to preventing the devastating personal, economic, and health consequences of preterm birth.This study shows the possibility of predicting preterm birth using a simple prediction model constructed from maternal characteristics. Thus, the optimal combination of maternal characteristics such as age (< 20), late initiation of ANC, unplanned pregnancy, recent pregnancy complications, multiparty, and hemoglobin < 11 mg/dl showed the possibility of predicting preterm birth. In addition, risk score calculations based on a combination of predictors were effective and had comparable accuracy with the model-based approach of the original β coefficients. It can be used in the clinical settings by healthcare providers for early detection, timely decision making, and improving care quality. They can then save lives.Below is the link to the electronic supplementary material.Supplementary Material 1: Supplementary Table 1. Bivariable logistic regression analysis of predictors of preterm birth among pregnant women who had ANC visit at DMCSH, 2020–2022


Mc-BTT at 36 wGA, though equal between groups, correlated in IUGR newborns with basal phosphate, mean total energy of the first week and month (positively) and days to reach full enteral feeding (negatively). Lower i.v. vitamin D intake, LLL and prolonged total parenteral nutrition predicted worse mc-BTT at 36 wGA in the enrolled infants. These results suggest that preterms and in particular IUGR newborns need special nutritional care to promote bone development.Intrauterine growth restriction (IUGR) together with preterm birth could be harmful to bone health. The aim of the study was to examine bone status in IUGR versus non-IUGR preterms and to analyze the nutritional management best correlated with its improvement. Newborns < 34 weeks of gestational age (wGA), 75 IUGR and 75 non-IUGR, admitted to the Neonatal Intensive Care Unit of the University Hospital of Padova were enrolled and monitored from birth until 36 wGA through anthropometry ), biochemistry, bone quantitative ultrasound assessment of bone status  and nutritional intakes monitoring during parenteral nutrition. IUGR compared to non-IUGR showed lower mean mc-BTT (0.45 vs. 0.51,  The goal of nutrition in preterm infants is to provide nutrients to enable their potential growth trajectory avoiding the possible deficiencies due to the premature interrupted placental supply. In fact, preterm infants are invariably deprived of the benefits of the last trimester of pregnancy , and above all, preterm infants with low birth weight (LBW) .Growing evidence suggests that LBW is associated with subnormal peak bone mass (PBM) and adults born preterm with very low birth weight (VLBW) display inferior bone mineral density (BMD) .As Balasuriya et al. observed in their study, the attenuated PBM in LBW infants is of concern, because PBM is an important determinant of fracture risk later in life  and, worThe combination of Intrauterine Growth Restriction (IUGR) and deprivation of the third trimester could be especially harmful for bone health.IUGR is estimated to affect between 5% and 10% of all pregnancies worldwide [This condition can strongly influence the newborn’s short- and long-term outcome as IUGR preterm newborns are exposed to a higher risk of complications than the non-IUGR premature infants of the same gestational age (GA), such as impaired physical growth, cognitive, and motor development . It is kThe accelerated postnatal growth and related insulin resistance is another characteristic of IUGR newborns , relatedAn impaired skeletal muscle development in IUGR was also reported in animal studies and related to altered protein synthesis .For all these reasons they could be even more sensitive to nutrients provision, though the ESPGHAN committee proposed the same nutritional management as appropriate for gestational age infants (AGA) due to the lack of data .An impaired fetal growth can also predispose to certain major diseases later in life, including metabolic syndrome and chronic lung and kidney diseases . In factThus, premature birth, especially in very-low-birthweight infants (VLBWIs), and restricted growth patterns could reduce bone accretion and deteriorate peak bone mass, resulting in an increased risk of future osteoporosis ,18.Few data exist on IUGR bone status. In mouse models the uteroplacental insufficiency in the IUGR condition predisposes to an endochondral ossification deficit at birth due to cellular changes within the growth plate, associated with reduced length, mineralization and strength of bone , but theTherefore, we aimed to examine bone status at birth in IUGR newborns compared to non-IUGR newborns and to analyze the postnatal nutritional management that best correlates with its improvement.Patients admitted to the Neonatal Intensive Care Unit (NICU) of the Women’s and Children’s Health Department of University-Hospital of Padova were prospectively enrolled in the study if they were <34 weeks of gestational age (GA), receiving total parenteral nutrition (TPN) within 48 h of life with informed consent obtained in the first 48 h for participation of a legally acceptable representative. IUGR newborns were matched with non-IUGR infants with the same GA, born in the same month and with the least difference in date of birth and days of GA. Exclusion criteria were a major congenital abnormality or a chromosomal abnormality, known or suspected congenital metabolic disease, congenital infections, neonatal death within 7 days of life and refusal of consent.Patients were monitored from birth through anthropometric, clinical and biochemical parameters. For each enrolled subject the following data were collected: demographic data such as GA, birth weight, length, head circumference, lower limb length, number of newborns small for GA, sex, pregnancy, type of delivery, antenatal steroids administration, Apgar score at 5 min and intrauterine growth restriction diagnosed by expert obstetricians based on prenatal ultrasonographic anthropometric and doppler parameters .Furthermore, for each enrolled subject, growth parameters during the neonatal intensive care unit (NICU) stay were collected. Growth was assessed through anthropometric measures: daily weight was registered and total length, head circumference, lower limb length at birth, 21 days of life and at 36 weeks of gestational age (wGA). A balance with the sensibility of one gram  or Tassinari Bilance ) was used to weigh preterm newborns. The total length and head circumference were obtained through a meter with the sensibility of 0.1 cm. Lower limb length was obtained in the same leg where mc-BTT was measured through a neonatal knemometer . Limb length was measured with the infant lying in the supine position, with 90° flexion at the hip and knee. Three readings for each measure were collected and the mean of these readings was calculated.Days to regain birth weight, minimum weight, maximum weight loss, and days to reach 1800 g were also evaluated. Biochemical data were collected, particularly serum calcium, phosphate and alkaline phosphatase at birth and at least weekly until 21 days of life or until the parenteral nutrition was discontinued. To assess bone status, we performed quantitative ultrasound (QUS) with DBM Sonic BONE PROFILER  within 48 h from birth, at 21 days of life and at 36 weeks of GA, which allows the evaluation of bone density, elasticity and bone structure. We tested the second metacarpus bone transmission time  as previously reported .At 36 wGA extrauterine growth restricted infant was defined as an infant with weight below the 10th percentile according to the Fenton Preterm Growth Chart .Standardized intravenous parenteral nutrition  was provided within 24 h of life following our protocol and in particular: ammino acids supplementation started at birth with 1.5–2 g/kg per day, advanced to a maximum of 3.5 g/kg per day with Non Proteic Energy started from 45 till 80 kcal/kg per day. Calcium and phosphorus intakes were between 0.8 and 2 mmol/kg per day. Parenteral magnesium and vitamin D administration were 0.15–0.2 mmol/kg and 30–160 IU/kg per day, respectively. The volume and number of feeds were decided by the attending physician. Briefly minimal enteral feeding was started in the first day of life, with human milk or formula then feedings were advanced at a rate of 10–20 mL/kg per day. As feeding advanced, TPN was decreased and stopped when enteral feeding reached 100–120 mL/kg per day.Nutritional intakes during parenteral nutrition were collected such as energy, protein, phosphorus, calcium and vitamin D intakes. Total energy, defined as the sum of energy provided by parenteral and enteral nutrition, was also calculated. Days of parenteral nutrition, time to full enteral feeding , and type of feeding after PN and at discharge were also recorded.Clinical outcomes were recorded such as incidence of respiratory distress syndrome (RDS), bronchopulmonary dysplasia (BPD), hemodynamically significant patent ductus arteriosus (PDA), intraventricular haemorrhage (IVH grade III or IV using the Papile classification), sepsis , necrotizing enterocolitis , retinopathy of the prematurity , cholestasis , days o® version 9.0 ,NCSS, LLC. Kaysville, UT, USA). Continuous variables were tested for normal distribution by applying the Shapiro–Wilk test before applying all subsequent statistical tests. Data were expressed as mean ± s.d. Student’s t-model was used to compare the different variables between the two groups under study (IUGR vs. non-IUGR). QUS data were analyzed both cross-sectionally and longitudinally, using univariate models. Pearson’s correlation was used to analyze relationships between variables. The existence of statistically significant differences between the two groups has been assessed by use of the chi-square for categorical variables. Finally, the median value of some variables was selected as a cut-off to divide the population into two groups whose mc-BTT at 36 GA was compared using Student’s t-test. A multivariate analysis of variance (MANOVA) was carried out in enrolled newborns in predicting/detecting the differences in the bone status. Significance was set at p < 0.05.Statistical analysis has been conducted by software NCSSThe study was approved by the Hospital Ethics Committee (reference number 4374/AO/17) and informed consent was obtained from the parents.Recruitment started January 2018 and lasted until December 2020. All subjects were recruited in the NICU of Women’s and Children’s Health Department of University-Hospital of Padova. A total of 354 preterm infants were enrolled and among these 81 were diagnosed with IUGR. A total of 75 IUGR were matched with 75 non-IUGR infants with the same GA and they were followed up until discharge from our unit. The main clinical features and auxological parameters at birth of the two cohorts of subjects are presented in At birth IUGR infants showed lower birth weight, total length, head circumference and lower limb length than non-IUGR infants . No diffDuring the NICU stay, they showed lower maximum weight loss, but they maintained a lower weight and more days to reach 1800 g. At term corrected age their weight, head circumference, total length and lower limb length continued to be lower .Analysis of biochemical data are reported in IUGR infants at birth and at 21 days of life had a worse bone status , while no differences were detected at 36 weeks of GA .p = 0.0518); protein intakes were not different  only in the IUGR group negatively correlated with QUS values on day 21 and at 36 weeks of GA. Mc-BTT at 36 wGA, only in the IUGR preterms positively correlated with basal phosphate, mean total energy of the first week and month and negatively with days to reach FEF.Searching for parameters that could be useful markers of bone status and using as the threshold value for each parameter the median of the distribution, mc-BTT at 36 weeks of GA was worse in newborns with lower limb length at 21 days < 91 mm, a mean energy intake in first week < 77 Kcal/kg/day or in first month of life < 106 Kcal/kg/day, an intravenous vitamin D intake in first week < 37 IU/Kg/day or a prolonged TPN (days ≥ 16) .Then we put together all the significant variables affecting bone health at 36 wGA to investigate which ones contribute independently to the mc-BTT value at 36 wGA with a multivariate analysis. TPN days, intravenously vitamin D intake in the first week of life and lower limb length at 21 days of life predicted bone status at 36 weeks of GA with the following model: BTT 36 weeks GA = 0.4962 + 0.0569 × (TPN days) + 0.0401 × (VitD iv 1week) + 0.0421 × (lower limb length 21 day) .A lower intravenously vitamin D intake in the first week, a lower limb length and prolonged TPN are associated with worse mc-BTT at 36 weeks of GA.Preterm infants are at risk for impaired bone mineralization and length later in life, due to the loss of last trimester mineral accumulation and the inadequate nutritional intake in the early postnatal period . ImproveGiven the same GA, anthropometric parameters of IUGR infants (including lower limb length) are lower at birth than non-IUGR and additionally they have a lower mc-BTT at birth and at 21 days of life.IUGR infants regain birth weight more quickly than non-IUGR infants; they reach a lower minimum weight during hospitalization but they take a longer time to reach 1800 g of body weight and have more days of hospitalization. The weight and total length of IUGR were lower in all measurement intervals, as also reported in the literature  and almoAnalysis of biochemical data showed in IUGR infants a lower plasmatic phosphate at birth, which can be related to an insufficient prenatal transfer to the fetus  and it cOf note, we found a statistically significant positive correlation between basal serum phosphate levels and mc-BTT at baseline and at 36 weeks of GA only in the group of IUGR newborns; additionally, phosphoremia at 21 days of life remains lower in IUGR and it is positively associated with BTT at 36 weeks of GA. These data suggest a role for phosphatemia in the first weeks of life in predicting in-patient bone status, with higher blood phosphorus levels being associated with better bone health, as reported in the literature [Furthermore, if mc-BTT values at baseline and at three weeks of life are lower in IUGRs than in non-IUGRs, since mc-BTT is closely associated with bone health, it can be inferred that being born IUGR is a risk factor for worse bone status at birth and in the first weeks of life .IUGR newborns received longer parenteral nutrition and reached full enteral feeding later. These data could reflect the caution of neonatologists in introducing enteral feeding due to the possible association between NEC and intrauterine growth restriction . NonetheSearching for nutritional variables that influenced bone status in IUGR infants, we found that prolonged periods of TPN correlate negatively with mc-BTT at three weeks of life and at 36 weeks of GA in both groups. In fact, prolonged periods of parenteral nutrition are associated with lower protein and energy intake, which may impair the synthesis of new bone tissue , while tIndeed, IUGR newborns received lower energy intake on average in the first week and in the first month of life (though the latter without significance), which could contribute to worsen bone status, as a better bone condition regards infants fed with higher energy intake from the first days of life . InteresA previous study conducted by our group  demonstrHigher intakes of vitamin D during the first week of life were associated with better ultrasonographic parameters at 36 wGA in both groups. Vitamin D is an important regulator of phospho-calcium metabolism. However, there is not a unique consensus in the literature on the vitamin D intake required for health bone condition ,43. NowaSearching for parameters that could be useful markers of bone status in all the enrolled patients, as mc-BTT was equal in both groups, we found that mc-BTT at 36 weeks of GA was worse in newborns that received a vitamin D intake in first week < 37 UI/Kg/day. Also, preterm newborns with a mean energy intake in first week < 77 Kcal/kg/die or in first month of life < 106 Kcal/kg/die, or with a prolonged TPN (days ≥16) had a worse mc-BTT at 36 weeks of GA.Interestingly, newborns with lower limb length at 21 days < 91 mm had a significantly worse mc-BTT at 36 weeks GA. Lower limb length was previously correlated with bone status at birth  and duriIt was finally possible to use a multivariate analysis of variance to show that low intravenous vitamin D intakes in the first week, shorter tibia length at 21 days and a long duration of TPN (≥16 days) are predictive for low BTT values at 36 weeks of GA.Our study has some limitations. It is a prospective observational study so we can just observe and report our findings without drawing firm conclusions. Another aspect is that we did not provide data on the follow-up after hospital discharge of our newborns. Nonetheless, we think it is useful to analyze bone status in preterm IUGR and detect the postnatal nutritional management that best correlates with its improvement.Our study suggests that IUGR infants could have a worse bone status at birth and a lower serum phosphate level in the first weeks of life despite adequate supplies.Mc-BTT at 36 wGA, though not different between groups, correlated in IUGR preterms positively with basal phosphate, mean total energy of the first week and month, and negatively with days to reach full enteral feeding.Low vitamin D intakes in the first week of life, shorter tibia length at 21 days and a long duration of parenteral nutrition (≥16 days) are predictive for lower BTT values at 36 weeks of GA.Our study suggests that preterms, and in particular IUGR infants, need special nutritional care to promote bone development. A larger study, comprehensive of long term follow up, will clarify the possible influence of nutrition on long term bone health.


Active electrolocation organ of weakly electric fish act as a proximity detection system with high accuracy in recognizing object parameters such as size and shape. In contrast, some fish with passive electrolocation organ are able to detect objects at a greater range. This paper proposes a joint active–passive electrolocation algorithm for long-range and high-precision underwater localization, inspired by the active and passive electroreceptive organs of fish. The study begins by designing a large experimental platform for the underwater localization system to investigate the response of underwater objects to active and passive electric fields. Based on the response, the paper proposes separate underwater active and passive electrolocation algorithms, which are then combined to form a joint algorithm. Experimental results demonstrate that the proposed algorithm achieves high localization accuracy and long detection distance. The joint active–passive electrolocation algorithm has potential applications in submarine resource exploration, underwater robotics, and maritime military projects, while also providing new ideas for future research on long-range underwater object detection and identification based on electrolocation. This has made it an indispensable and significant aspect of social life. However, although there are numerous positioning technologies available, most are only applicable to terrestrial or aerial environments. Given the complexity of the underwater environment, many of the existing positioning technologies are no longer suitable. As one of the important technologies for exploring underwater environment, underwater positioning technology has great application value in underwater resource exploration, underwater military construction and underwater robot navigation. In the ocean, the main technologies that can achieve positioning are acoustic, optical and GPS7. The advantages of acoustic methods are long positioning distance and mature technology. However, the acoustic method is easily affected by the environmental conditions of the water body, such as the thermocline in the ocean, bottom characteristics, tidal velocity, etc. The optical method can reach the centimeter level in close range positioning, but the method is greatly affected by the clarity in seawater. GPS has the advantages of high positioning accuracy, good flexibility and easy operation on land. However, its signal cannot be propagated in the underwater environment, and the realization of underwater positioning requires the use of other equipment, which makes the positioning cost increase8. Due to the limitation of these methods in underwater environment, we need to study other methods suitable for underwater positioning.As society progresses and science and technology continue to develop, positioning technology is becoming increasingly sophisticated. Various forms of positioning technology, such as satellite positioning technology, base station positioning technology, and Wi-Fi positioning technology9. This means that understanding and imitating the localization mechanism of weak electric fish can provide us with a new underwater localization method.In fact, as early as the middle of the last century, biologists discovered a primitive sense for underwater localization, which came from a fish in nature, the weakly electric fish. Weakly electric fish can recognize their external environment in the dim, murky ocean in order to hunt for prey and avoid obstacles and natural predators because of a special ability they have, namely, electrolocation. Weakly electric fish have organs on their tails composed of special electric disclike cells called electric organ discharges (EOD), which can emit a voltage signal of a certain size to create an electric field around their bodies. Its body epidermis is distributed with electric field receptors, which are extremely sensitive, complex and practical, almost replacing the function of vision and smell in their daily work11. Active electrolocation is that the weakly electric fish establishes a low-frequency electric field around its body through the EOD and receives the electric field through the electric field receptors. When an object enters the electric field area established by the weakly electric fish, the electric field will change, and the changed electric field signal is received by the electric field receptor organ of the weakly electric fish and transmitted to the brain of the weakly electric fish for processing and analysis, so as to recognize the existence of the object and determine the location of the object, but the locating range is very limited12. This is because the active electric field emitter is like a pair of electric dipoles, and the intensity of the generated electric field decays with the cube of the distance13. Passive electrolocation are widely found in weakly electric fish, rays, sharks, catfish, and cetaceans, which sense electric field signals from the outside world through their electric field receptors and then identify and locate the object emitting the electric field signal17. In contrast to active electrolocation, passive electrolocation has a much wider effective range, and these fish are able to locate objects and communicate with other fish at distances greater than the active electrolocation distance18. Because the electric field received by passive electrolocation is generated by other organisms, the magnitude of this electric field is inversely proportional to the square of the distance19. Inspired by the above electric field perception of fish, many methods of underwater electric field detection have been proposed. For example, in active electric field detection, Bai et al. proposed an algorithm implemented in a robotic active sensing system that can recognize the size, shape, orientation and position of ellipsoidal objects with high localization accuracy, but the recognition range is only 20 cm20. Jiang et al. proposed an underwater moving object localization method based on the active electric field induction principle and long and short term memory network, and the experimental results show that the method has a two-dimensional average absolute localization error of 5.38 mm and a two-dimensional average relative localization error of 1.06% in a 500 mm 21. In terms of passive electric field detection, Cho et al. proposed a real-time underwater object detection method using the geophysical direct current resistivity technique, and validated it in the field at sea. The method belongs to passive electric field detection, which can detect moving objects at a distance of 1 m away in real time, and the distortion response becomes clear when the excitation current increases to more than 20 A22. Jun-Zheng Zheng et al. proposed a localization scheme for a small underwater robot that swims freely in a large-scale environment23. The localization distance of this scheme is related to the designed transmitter, whose maximum detectable range is about 2.32–2.76 m, and the error has a large impact on the positioning accuracy. In summary, the active electric field can detect the size and shape of an object and can locate the position of the object, but the electric field strength decreases rapidly with distance, making the effective localization distance very limited. Compared with the active electric field, the passive electric field has a wider localization range, but its localization accuracy is lower.There are two types of electrolocation, active and passive24. In addition, Shang et al. proposed an underwater target localization method combining the subspace scanning algorithm and the meta-evolutionary programming particle swarm optimization algorithm, and they used this method to simulate and analyze the localization performance of uniform circular and cross-shaped electrode arrays, and the results showed that the method can effectively improve the localization accuracy without changing the localization accuracy and search speed25. In terms of active electric field localization, Yesol et al. proposed an algorithm that can decompose the measurement signal into multiple frequency coefficients and verified the feasibility of this algorithm in a localization system designed by them, in which the sensing arrays are multiple receiving electrodes arranged in a line11. Sylvain Lanneau et al. designed an algorithm that can be used for shape estimation and location localization of an elongated probe consisting of four metal rings and used a multi-signal classification algorithm to localize objects in a localization study26. Xu et al. proposed an active localization method based on a hybrid polarization multi-signal classification algorithm, and simulations and experimental analyses of a uniform circular antenna were performed to verify the effectiveness of the method27.Due to the complexity and uncertainty of the underwater environment, underwater electric field localization faces many challenges, such as changes in water temperature, salinity, and current velocity. To overcome these challenges, algorithms need to be designed to handle these complexities and improve the accuracy and reliability of underwater electric localization. Algorithms can extract useful information from the signal and reduce the influence of interfering signals to achieve accurate localization and tracking of underwater targets. Due to the relatively late development of underwater electric field localization technology, the research value of this technology is still to be explored, which makes that so far there are still only a few researchers engaged in this area of research, and even fewer research on underwater electric field localization algorithms, and most of the localization algorithms are designed according to the specific localization system in their research, and their universality is still to be further studied. For instance, in passive electric localization, Kim et al. proposed an axis calibration method for underwater electric field measurement sensors, and for the sensing array they designed, they used the Levenberg optimization algorithm to optimize the measured values, and the simulation results show that the algorithm can effectively reduce the localization error of their proposed axis calibration method for underwater robots28, so the performance of the time-domain localization algorithm is easily affected by the environmental noise. In order to improve the anti-interference ability, robustness and stability of the underwater electric positioning system, our group designed a one-dimensional underwater frequency domain positioning algorithm based on FFT feature extraction29. On this basis, our team proposed a two-dimensional underwater active electrolocation system and studied three frequency domain localization algorithms, namely cross location algorithm, stochastic location algorithm and particle swarm optimization algorithm30. By comparison, the particle swarm optimization algorithm has the best overall performance. However, although the system based on particle swarm optimization algorithm can locate the underwater target, the algorithm is prone to fall into local optimum, which leads to the failure of localization. Therefore, we need to improve the algorithm or find other algorithms that are more suitable for controlled underwater electric field localization systems.All of the above positioning methods are realized by designing different sensing arrays and using corresponding algorithms to locate the object or the positioning system itself. And our team mainly studies the controlled underwater electric field localization system, that is, by controlling the movement of the localization system, so that it is constantly close to the measured object, and when the movement of the localization system is within a certain range near the measured object, it indicates that the localization is successful. In terms of localization algorithms, our team has also conducted extensive research on algorithms applicable to controlled underwater electric field localization systems. In the preliminary research, our group found that the feature maps based on the time-domain data processing method would generate a lot of burrs due to the presence of environmental noiseIn this paper, we propose a novel approach to overcome the limitations of both active and passive electrolocation by combining their strengths. Our idea is based on the team’s extensive research on bionic systems and the development of a large experimental platform for the study of underwater electrolocation. The response characteristics of both active and passive electric fields have been thoroughly studied using the experimental platform, and a joint active–passive electrolocation algorithm has been designed based on these findings. The algorithm incorporates three nature-inspired optimization algorithms, namely particle swarm optimization (PSO), simulated annealing (SA), and hill climbing. Among them, PSO and SA form the PSOSA combined optimization algorithm, which improves on the separate PSO and SA, and is used for localization when the active electric field is operating in the combined active–passive electrolocation system. Whereas, the hill climbing algorithm is used for localization during passive electric field. The feasibility of the proposed algorithm has been verified through extensive experiments, and the results indicate that it can accurately position underwater targets over long distances. This combination of active and passive electrolocation provides a promising solution to address the challenges faced by traditional electrolocation methods.To achieve underwater electric field localization, we built an underwater electrolocation system. The schematic diagram of the experimental platform is shown in Fig. mentclass2pt{minimAs mentioned earlier, electric fish can sense the presence of objects in the water using low frequency electric fields. When an object enters the electric field environment established by the electric fish, it distorts the electric field and produces a corresponding change in voltage amplitude, which is then sensed by the electric fish. In addition, the electric fish can determine the direction and position of the object and localize it by detecting the weak electrical signals emitted by the object. In order to study the response characteristics of the object under test to active and passive electric fields, we placed a solid aluminum cylinder as a target object in a pool and measured the voltage amplitude around it by controlling the motion of the underwater electric positioning system. Figure In the test of the response of the target object to the passive electric field Fig. , the tarThe experimental results reveal that the response characteristics of the active electric field exhibit rapid fluctuations in proximity to the object, and exhibit minimal variations at a distance, which hinders its utility for long-range positioning. In contrast, the response characteristics of the passive electric field exhibit a desirable curve, with a clear gradient of the electric field that varies with distance, making it favorable for long-range positioning. Subsequently, we shall investigate localization algorithms that are based on the distinct response characteristics of the electric field.From the previous section, it can be seen that there are significant differences in the response of the object under test to the active and passive electric fields. Therefore, in this section, the corresponding localization algorithms will be designed based on the characteristics of the object’s response to active and passive electric fields obtained in the previous section to realize active and passive electrolocation, respectively. Then, combining the active electric field’s ability to recognize more information about the object and the passive electric field’s ability to increase the detection distance, the two algorithms are combined to form a joint active–passive electrolocation algorithm.In accordance with the response properties of underwater target objects within an active electric field, the precise localization of such objects can be achieved by identifying the nadir of the underwater electric field response value. This enables the transformation of the active electric field localization quandary into an optimization problem. An optimization problem, by definition, involves determining the maximum or minimum value of a particular objective function under specified constraints. Bionic algorithms, as heuristic methodologies derived from natural phenomena and biological entities, closely emulate natural behaviors. Since the design of the underwater active electric field system is inspired by biological organs, the use of bionic algorithms in active electric localization is a reasonable and effective choice. Through the bionic algorithm, the behaviors and mechanisms of living organisms can be simulated, which makes the positioning system more in line with natural behaviors and improves the accuracy and adaptability of positioning. In addition, since the expression of the objective function in the localization process is unknown, traditional optimization algorithms may not be directly applied. Therefore, the use of meta-heuristic bionic algorithms is a successful way to achieve localization. The meta-heuristic bionic algorithm is able to simulate the behavior of organisms in nature and gradually approach the optimal solution by searching and optimizing, so as to achieve the localization goal in the case of unknown objective function.32. The SA is inspired by the behavior of solid matter in bionics as it changes with temperature. In optimization problems, the SA searches for the global optimal solution by the degree of random variation of states in the temperature control system. The basic idea is to introduce a certain degree of randomness in the search process in order to overcome the distress caused by the multi-peaked problem, so that the algorithm has enough chances to jump out of the local minima and find the global optimal solution. SA has the advantages of being simple and easy to implement, and having a strong ability of global search, however, the algorithm is slow to converge and requires a large amount of computational resources.GA are inspired by the evolutionary theory in bionics. In optimization problems, GA is an optimization algorithm that simulates the mechanism of natural selection and genetic inheritance, and gradually optimizes the population by three basic operations: selection, crossover and mutation, so that the population gradually converges to the global optimal solution. Its greatest advantage is its parallelism, which is suitable for solving optimization problems with high dimensionality, but its convergence speed is slow. PSO is inspired by the collective behavior of flocks of birds or schools of fish in bionics. In the optimization problem, the PSO achieves efficient solution of the optimization problem by simulating the process of “individual” following and “group” learning and collaboration of particles. The basic idea is to treat each individual as a particle, and use the historical optimal position and the global optimal position as a guide to continuously adjust its own position and speed, so that the algorithm gradually approximates the global optimal solution. Its advantages are simple and easy to implement, better adaptability to the constraints of the problem, and parallelizable processing, however, the algorithm is sensitive to the local optimal solution of the problem, and it is easy to fall into the local optimal solution.At present, the common meta-heuristic algorithms include simulated annealing algorithm (SA), genetic algorithm (GA) and particle swarm optimization algorithm (PSO)Its advantages are simple and easy to implement, better adaptability to the constraints of the problem, and parallelizable processing, however, the algorithm is sensitive to the local optimal solution of the problem, and it is easy to fall into the local optimal solution. The response characteristic value of underwater active electric field is taken as the model of optimization problem. In the simulation experiment, 0.027 V, minimum value, is used as the criterion to judge whether the positioning is successful. Three algorithms, PSO, GA and SA, are simulated for 1000 times respectively. The success rate of the three algorithms in 1000 simulation tests, as well as the fastest, slowest and average iterations when successful are analyzed and explored. The simulation results are shown in Table From Table n particles in 2D search space.Randomly generate the velocity and position of According to the underwater electric field characteristic response value at the position of each particle, the fitness value of each particle is evaluated, and the historical optimal position of each particle and the global optimal position of the swarm are obtained according to the fitness value.34: Update the velocity and position of particles according to the velocity update Eq.  and the d is the dimension of search space; k is the iteration of the particle; i; (d)Evaluate the fitness value of particle swarm.(e)Calculate the fitness value difference between the new position and the old position of each particle: n in Eq. :(f)Stop if the solution is good enough or reaching the maximum iteration. Otherwise, reduce the temperature according to the cooling method, update the weight value and go back to step (b).In PSO, each particle will accept the new position calculated according to the position update formula with a 100% probability. In PSOSA combined optimization algorithm, Metropolis criterion will determine the probability of accepting new position according to the difference between the fitness values of the new position and the old position of the particle. This optimization makes it easier for the particle swarm to jump out of the local optimal solution in the update process, and the fluctuation of the overall fitness value will be reduced when the search approaches the end. Selecting the appropriate cooling method in the optimized algorithm determines the quality of the algorithm in the optimization process. Generally, when the search starts, the particle is in the highest temperature state, and will accept the bad new state with a great probability, so it is easy to jump out of the local optimal solution. When the temperature gradually decreases to the final stable state, the probability of the particle accepting the bad state is very low, so the particle can finally be in a relatively stable state. The cooling method selected in the PSOSA combined optimization algorithm is shown in Eq. .The flow of the hill climbing algorithm is shown in Fig. The basic steps of hill climbing algorithm are as below: To prove the feasibility of hill climbing algorithm, we carried out simulation experiment on the hill climbing algorithm, taking the response characteristic value of passive electric field as the simulation model. In the simulation, the starting position is random, and the positioning is regarded as successful when the algorithm searches the highest point.After 1000 simulation experiments, we obtained the following results .Update the velocity and position of particles. Compare the fitness value of particles, and then update the optimal fitness value and optimal position of particles and swarm. Move the positioning device to the optimal position of swarm.Stop if the solution is good enough or reaching the maximum iteration. Otherwise, go back to step (d). The process of joint active–passive electrolocation algorithm is shown in Fig. The basic steps of the algorithm are as below: By combining passive and active electrical localization, we can search for a target over a wider range and acquire more information for more accurate localization.To validate the feasibility of the designed hill climbing algorithm, PSOSA combined optimization algorithm and joint active–passive electrolocation algorithm, we applied them to our designed underwater electrolocation system for underwater electrolocation experiments, respectively. In the experiment, both the transmitting and receiving electrodes are mounted on a bracket with a length of 270 mm In the experiments with the PSOSA combined optimization algorithm, the initial position of the positioning device is also used as the coordinate origin of the 2D detection plane. The target object is an aluminum cylinder placed at the center of the 100 cm In the localization experiments using the hill climbing algorithm, the initial position of the localization device was used as the coordinate origin of the two-dimensional detection plane. The target object was located at the center of the 140 cm In the experiments of the joint active–passive electrolocation algorithm, to highlight the localization performance of the algorithm, the initial position of the localization device was set at a distance of 4 m from the target object. Considering that the size of the pool is 4 m In the experiments of joint active–passive electrolocation algorithm, the hill climbing algorithm is firstly used to search the signal source location to make the localization device move near the target, and then switch to the PSOSA combined optimization algorithm to make the localization device move to the target location. A typical localization path in the experiment is shown in Fig. The above three experiments verified the feasibility of the three algorithms designed for underwater localization. The results show that all three algorithms can successfully localize the target object in no more than 16 iterations, which proves that they have faster localization speed. Among them, the number of iterations of the joint active–passive electrolocation algorithm is higher than that of the independent hill-climbing algorithm and the PSOSA combined optimization algorithm. This is because the joint active–passive electrolocation algorithm, which is a combination of the latter two algorithms, was experimented in a larger pool, and a larger experimental range may increase the number of iterations. However, compared to the other algorithms listed in Table During the experimental localization process, the position of the passive electric field switching to the active electric field is controlled by human. Under our research conditions, as shown by the simulation results in Table We only demonstrated the results of the algorithm in a pool of size 4 m 36. However, according to the mechanism of the algorithm, even in seawater, a charged object in water can still be successfully localized using the hill-climbing algorithm; the PSOSA combined optimization algorithm can still successfully localize an object with similar characteristics to the response of an aluminum cylinder to an active electric field in freshwater , so that the underwater robotic working unit moves to the vicinity of the target object, and then switches to active electrolocation, so that the underwater robotic working unit can accurately locate the target and then recover it.In summary, the algorithm designed in this paper is an effective underwater electrolocation algorithm.In the present study, we design a novel joint active–passive electrolocation bionic algorithm that can be used for underwater high-precision and long-range positioning based on the characteristics of active electrolocation with high positioning accuracy and passive electrolocation with long positioning distance. One of the passive electrolocation component employs a hill climbing algorithm to facilitate long-range localization, whereas the active electrolocation element utilizes a particle swarm optimization simulated annealing (PSOSA) combined optimization algorithm for proximity positioning in order to improve the positioning accuracy. Furthermore, the PSOSA combined optimization algorithm, an amalgamation of the particle swarm optimization algorithm and the annealing algorithm, demonstrates a high localization success rate and reduced average iteration count. The practicability of the hill climbing algorithm, the PSOSA combined optimization algorithm, and the joint active–passive electrolocation algorithm is corroborated through a series of experiments. The empirical findings indicate that the suggested algorithms yield considerable localization range and accuracy, rendering them suitable for applications in underwater robotics, search and rescue operations, and naval military endeavors. Nevertheless, the localization velocity of the proposed algorithm is inferior to that of the hill climbing algorithm and the PSOSA combined optimization algorithm individually. Future research will be conducted to examine the localization speed in greater depth.All authors agree to publication.


We assessed the relationship between hospital characteristics and risk of adverse birth outcomes among minority Anglophones in Montreal, Canada.The study included 124,670 births among Anglophones in metropolitan Montreal between 1998 and 2019. We estimated risk ratios (RR) and 95% confidence intervals (CI) for the association between hospital characteristics, including residential proximity to hospitals and language in which medical services are provided, and risks of preterm birth and stillbirth. Models were adjusted for maternal socioeconomic status and other characteristics.In this study, 8% of Anglophones had a preterm birth and 0.4% a stillbirth. Anglophone women who delivered at a farther French hospital had a greater risk of stillbirth  than preterm birth , compared with delivery at hospitals closer to home. In contrast, delivery at a farther English hospital was associated with similar risks of stillbirth  and preterm birth . The greater risk of stillbirth with delivery at a farther French hospital, versus greater risk of preterm birth at a farther English hospital, remained present in analyses stratified by maternal age, education, material deprivation, and region of origin.Minority Anglophones in Montreal who travel to a farther French hospital for delivery have a greater risk of stillbirth than Anglophones who travel to a farther English hospital. This novel observation suggests the need to determine if access to perinatal healthcare in a woman’s language may help reduce the risk of stillbirth. Suboptimal access to perinatal care is associated with preterm birth and stillbirth among ethnocultural minorities with language barriers –4. CommuTo improve communication with providers, women from cultural minorities may choose to travel farther to obtain perinatal care in their language or have the benefit of translation services. This is particularly the case in Montreal, Quebec, where Anglophones are a minority and represent only 13.2% of the population . Most hoWe analyzed a population-based cohort of 124,670 births among Anglophone women residing in the metropolitan centre of Montreal, Quebec, Canada between 1998 and 2019. Anglophones are the most prevalent linguistic minority in Quebec after Francophones Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: No**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: No**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: No**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The authors presented some interesting data on whether the hospital’s primary language of service (French or English) may affect birth outcomes for mothers whose native language is English (Anglophone) – a large minor language group in Montreal. They found that minority Anglophones in Montreal who travel to a farther French hospital for delivery have a greater risk of stillbirth than Anglophones who travel to a farther English hospital. Distance to the place of delivery affect birth outcomes. Delivery at a farther hospital (whether English or French) was associated with higher risks of preterm birth and stillbirth than delivery at a hospital close to home. The study is based on a large birth cohort including 124,755 births among Anglophones in metropolitan Montreal.In general, the study was well conducted, and the manuscript is well-written.I only have some minor edits:1. Title, change “birth outcome” to “birth outcomes”2. Abstract, line 23, change “risk of ” to “risks of”3. Abstract, last sentence, change “More effort is needed” to “This novel observation suggests the need to”4. Keyword: change “Premature birth” to “preterm birth”5. Introduction, line 43, change “one systematic review” to “a systematic review”6. Page 8, Table 1, for civil status, I believe “married” should be “married or common-law union”, and for education, delete “training” after “University”7. Discussion, page 14, line 241, change “Risk of preterm birth and stillbirth was greater …” to “Risks of preterm birth and stillbirth were greater …”Reviewer #2: This study examined and compared differences in PTB and stillbirth occurrences between Anglophone women who delivered at a farther English speaking or French speaking hospital (than the closest available from their home) and those who delivered at the closest hospital in Montreal, Canada. The study was based on 124,755 births in 1998 – 2019 identified from live birth and still registration databases to identify maternal mother tongue (Anglophone or not), postal code of residence, hospital of birth, and the study outcomes, and found the risks of PTB and stillbirth were slightly higher among women who delivered at a farther hospital. The study needs to address some important points to clarify its importance and contributions.The exposure of interest is not clearly defined in regard to what it represents. What is the exposure of interest exactly? The authors discussed several points related to traveling to a hospital for delivery in remote distance in Discussion, but the study exposure is not about how far but the primary language of service at a farther hospital. Communication barriers is one of things that the authors hint what it might capture, but the exposure variable used in the study does not necessarily capture that. The mother tongue (English) recorded on the birth certificate does not necessarily mean that the mother is not capable of communicating in French. Even if it is the case, their spouse or family member accompanying might be able to communicate in French, and the doctors/staffs can communicate in English at a “French” hospital .In addition, it is unclear how communication language during labour and delivery would affect preterm birth and stillbirth? The authors claim that “[C]ommunication barriers among Anglophones in French hospitals have the potential to lead to delays in labor induction or use of cesarean section to prevent stillbirth.” in Discussion. (p.13) However, this requires the stillbirth outcome of the study mainly to be the death during labour, which is not the case according to its definition in the study (see my comment below). There is also no support in the study to conceptualize the delivery hospital was the hospital where the mothers had prenatal care all along.The stillbirth is defined based solely on the birth weight criterion (<=500g) that is the provincial criterion. However, it is well known that it includes both viable and non-viable births, and late pregnancy termination, thus a combination of true fetal deaths and elected late abortion. This is also the case for a stillbirth definition by gestational age that is commonly used. The authors mainly discuss the outcome of stillbirth as a proxy of death during labour/delivery, but the actual measure used is far from that. Please clarify what is the stillbirth outcome that is hypothesized to be affected by the language and distance of a delivery hospital.The main conclusion is quite misleading: “This study of the Anglophone minority in metropolitan Montreal found that women who traveled to a farther French hospital had a greater risk of stillbirth, while women who traveled to a farther English hospital had a greater risk of preterm birth.” This conclusion is mainly drawn from the difference in point estimates it seems. The Cis overlap with each other and the authors also stated that the differences across outcomes were not statistically significant (p.13). Even if they were, the differences are so minimal to have meaningful impacts—They did not present differences on absolute scale, but the stillbirth was very rare in occurrence (Table 1). In addition, as described above, there are insufficient explanations and data to support their explanations/arguments.Please clarify whether the analysis was restricted to singleton births where both PTB and stillbirth risks are higher. Also, there would be multiple births to the same mother over the study period, 1998 – 2019. Please show the extent of clustering at mother and how it was accounted for.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  15 Mar 2023RESPONSE TO REVIEWACADEMIC EDITOR:E1.1. Kindly refer thoroughly to the reviewers' comments, especially reviewer's 2 comments on statistical issues. We ask you to give a special attention to the methodology including the statistical analysis, the data provided as basis for the findings, and the results' presentation.Response: We thank the Editor. We clarified the statistical analysis, basis of the data, and presentation of results throughout, and did our best to address other points raised by the Reviewers.Reviewer #1:The authors presented some interesting data on whether the hospital’s primary language of service (French or English) may affect birth outcomes for mothers whose native language is English (Anglophone) – a large minor language group in Montreal. They found that minority Anglophones in Montreal who travel to a farther French hospital for delivery have a greater risk of stillbirth than Anglophones who travel to a farther English hospital. Distance to the place of delivery affect birth outcomes. Delivery at a farther hospital (whether English or French) was associated with higher risks of preterm birth and stillbirth than delivery at a hospital close to home. The study is based on a large birth cohort including 124,755 births among Anglophones in metropolitan Montreal.R1.1. In general, the study was well conducted, and the manuscript is well-written.Response: We thank the Reviewer.R1.2. Title, change “birth outcome” to “birth outcomes”.Response: We modified the title (line 1).R1.3. Abstract, line 23, change “risk of ” to “risks of”.Response: We made the requested change (line 23).R1.4. Abstract, last sentence, change “More effort is needed” to “This novel observation suggests the need to”.Response: We corrected the sentence (line 35).R1.5. Keyword: change “Premature birth” to “preterm birth”Response: We replaced the keyword (line 38).R1.6. Introduction, line 43, change “one systematic review” to “a systematic review”.Response: We made the correction (line 43).R1.7. Page 8, Table 1, for civil status, I believe “married” should be “married or common-law union”, and for education, delete “training” after “University”.Response: As the data were based on the formal definition of civil status, we referred to legally married women only. We clarified that we included “legal civil status ” (line 116). We also deleted the term training from Table 1.R1.8. Discussion, page 14, line 241, change “Risk of preterm birth and stillbirth was greater …” to “Risks of preterm birth and stillbirth were greater …”Response: We modified the sentence and thank the Reviewer (line 254).Reviewer #2:R2.1. This study examined and compared differences in PTB and stillbirth occurrences between Anglophone women who delivered at a farther English speaking or French speaking hospital (than the closest available from their home) and those who delivered at the closest hospital in Montreal, Canada. The study was based on 124,755 births in 1998 – 2019 identified from live birth and still registration databases to identify maternal mother tongue (Anglophone or not), postal code of residence, hospital of birth, and the study outcomes, and found the risks of PTB and stillbirth were slightly higher among women who delivered at a farther hospital. The study needs to address some important points to clarify its importance and contributions.Response: We thank the Reviewer and clarified that “The findings highlight the importance of communication in perinatal healthcare for minority language groups. Having access to a hospital that provides care in the main language of choice has potential to reduce the risk of stillbirth” (lines 310-313).R2.2. The exposure of interest is not clearly defined in regard to what it represents. What is the exposure of interest exactly? The authors discussed several points related to traveling to a hospital for delivery in remote distance in Discussion, but the study exposure is not about how far but the primary language of service at a farther hospital. Communication barriers is one of things that the authors hint what it might capture, but the exposure variable used in the study does not necessarily capture that. The mother tongue (English) recorded on the birth certificate does not necessarily mean that the mother is not capable of communicating in French. Even if it is the case, their spouse or family member accompanying might be able to communicate in French, and the doctors/staffs can communicate in English at a “French” hospital .Response: The exposure of interest is the language of the hospital. English mother tongue is an inclusion criterion rather than an exposure. We clarified that “The main exposure measure was the primary language of the hospital used for delivery. Women could deliver at the hospital closest to their residence or at a farther hospital that offered either English or French perinatal services. Although French hospitals can provide care in English, staff may not be as fluent or find it as simple to identify urgent care needs. Ease of communication may not be as fluid for Anglophone women requiring tertiary care at a French hospital” (lines 77-81). We also added that “The association was present despite the potential ability of Montreal Anglophones to communicate in French to some degree, or present to hospital with an English-speaking spouse or family member. As a result, the findings suggest that basic knowledge of French is insufficient to communicate or understand complicated medical terminology” (lines 220-224). The ease with which a woman may converse in French varies from person to person, whether or not a spouse or family member is present. Some Anglophones are comfortable using French, while others are not. Ability to understand and speak French does not mean that there is full comprehension of the language. We added the limitation that “We could not determine whether women were comfortable understanding or conversing in French” (lines 294-295).R2.3. In addition, it is unclear how communication language during labour and delivery would affect preterm birth and stillbirth? The authors claim that “[C]ommunication barriers among Anglophones in French hospitals have the potential to lead to delays in labor induction or use of cesarean section to prevent stillbirth.” in Discussion. (p.13) However, this requires the stillbirth outcome of the study mainly to be the death during labour, which is not the case according to its definition in the study (see my comment below). There is also no support in the study to conceptualize the delivery hospital was the hospital where the mothers had prenatal care all along.Response: The Reviewer is correct that some fetal deaths may occur before the onset of labor. However, stillbirth during labor is not the outcome of interest. Prevention of stillbirth begins before the onset of labor, as women may experience antepartum signs and symptoms that predict fetal death. Pregnant women are instructed to call the hospital for any problem or emergency. Women who incorrectly communicate their problem or incorrectly interpret medical advice are at risk if there is a delay in labor induction. We clarified that “Stillbirths include fetal deaths that occur antepartum or intrapartum. Antepartum fetal deaths can potentially be prevented with labor induction, whereas intrapartum deaths can be prevented with cesarean delivery” (lines 104-106). The delivery hospital is decided by the physician prenatally. The physician follows the patient in the prenatal period, and sends the contents of the obstetric chart to the hospital before delivery. The patient delivers at the designated hospital where the chart is located, rather than at another hospital.R2.4. The stillbirth is defined based solely on the birth weight criterion (<=500g) that is the provincial criterion. However, it is well known that it includes both viable and non-viable births, and late pregnancy termination, thus a combination of true fetal deaths and elected late abortion. This is also the case for a stillbirth definition by gestational age that is commonly used. The authors mainly discuss the outcome of stillbirth as a proxy of death during labour/delivery, but the actual measure used is far from that. Please clarify what is the stillbirth outcome that is hypothesized to be affected by the language and distance of a delivery hospital.Response: We agree with the Reviewer and have removed late pregnancy terminations from the analysis. We updated all the results in the manuscript and have clarified that stillbirth was defined “as the intrauterine death of a fetus weighing 500g or more that was not due to voluntary termination” (lines 108-109). This change did not alter the interpretation of results.We addressed viable and non-viable stillbirths as follows: “Stillbirths include fetal deaths that occur antepartum or intrapartum. Antepartum fetal deaths can potentially be prevented with labor induction, whereas intrapartum deaths can be prevented with cesarean delivery” (lines 104-106). In the discussion, we clarified that labor induction and cesarean section can be used to prevent “antepartum or intrapartum stillbirth” (line 243).R2.5. The main conclusion is quite misleading: “This study of the Anglophone minority in metropolitan Montreal found that women who traveled to a farther French hospital had a greater risk of stillbirth, while women who traveled to a farther English hospital had a greater risk of preterm birth.” This conclusion is mainly drawn from the difference in point estimates it seems. The Cis overlap with each other and the authors also stated that the differences across outcomes were not statistically significant (p.13). Even if they were, the differences are so minimal to have meaningful impacts—They did not present differences on absolute scale, but the stillbirth was very rare in occurrence (Table 1). In addition, as described above, there are insufficient explanations and data to support their explanations/arguments.Response: The conclusion is drawn from the associations for women who deliver at a farther French hospital, where the risk of stillbirth is higher than the risk of preterm birth. We restructured Table 5 to make the comparison of these outcomes clearer. Table 5 shows that among French hospitals, Anglophones in every stratum always have a higher risk of stillbirth than preterm birth. This pattern is absent in English hospitals, where the risk of stillbirth is either the same as preterm birth, or sometimes higher or lower. Focusing on statistical significance is not recommended in the literature . For this reason, we do not feel justified suggesting that women who deliver at a farther French hospital have similar risks as those who deliver at a farther English hospital. Instead, we modified the conclusion as follows: “This study of the Anglophone minority in metropolitan Montreal found that women who traveled to a farther French hospital had a greater risk of stillbirth than preterm birth, whereas women who traveled to a farther English hospital had no meaningful difference in these outcomes” (lines 305-307).Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016;31(4):337–50.Wasserstein RL, Schirm AL, Lazar NA. Moving to a world beyond “p < 0.05.” Am Stat. 2019;73(sup1):1–19.R2.6. Please clarify whether the analysis was restricted to singleton births where both PTB and stillbirth risks are higher. Also, there would be multiple births to the same mother over the study period, 1998 – 2019. Please show the extent of clustering at mother and how it was accounted for.Response: The analysis included all births, whether singleton or higher order. Unfortunately, attempts to adjust for multiple birth did not allow models to converge, likely because multiple birth was rare (3.6% of births were multiple). As we did not have a unique patient identifier, we also could not adjust for clustering within mothers. We added the limitation that “We could not adjust for multiple birth or the added variance due to clustering of births within mothers” (lines 297-298). We thank the Reviewer.AttachmentPONE-D-22-31177-R1_Auger_Response to Reviewers.docxSubmitted filename: Click here for additional data file. 4 Apr 2023Access to perinatal healthcare in minority Anglophones: Hospital type and birth outcomesPONE-D-22-31177R1Dear Dr. Auger,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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