We evaluated the prevalence of preterm birth (PTB) and very low birth weight (VLBW) during Jan-Dec 2,020 (early COVID era) at 5 hospitals  compared to Jan 2017–Dec 2019 (pre-COVID) inclusive of 2 regional perinatal centers  and 3 community hospitals .We examined PTB and VLBW rates of live births at 5 US hospitals from Jan 2017–Dec 2020. We compared PTB and VLBW rates in 2020 to 2017–2019 using Poisson regression and rate ratio with a 95% confidence interval. We stratified live births by gestational age (GA)  and birth weight . We examined PTB rates at 4 of the hospitals during Jan-Dec 2020 and compared them to the prior period of Jan 2017–Dec 2019 using Statistical Process Control (SPC) for quarterly data.p = 0.222). Additionally, there was no significant change when comparing VLBW rates in 2017–2019 to 2020, 1.4% in 2017–2019 vs. 1.5% in 2020 (p = 0.832).We examined PTB and VLBW rates in 34,599 consecutive live births born Jan 2017–Dec 2019 to rates of 9,691 consecutive live births in 2020. There was no significant change in PTB (<37 weeks GA) rate, 10.6% in 2017–2019 vs. 11.0% in 2020 (We found no significant change in the rates of PTB or VLBW when combining the live birth data of 5 US hospitals in 3 different counties. Early in the SARS-CoV-2 pandemic, regional preterm birth (PTB) prevalence of very low birth weight (VLBW) infants in a designated Irish region reported temporal reduction from ∼8 to ∼2/1,000 live births . In contWe examined PTB and VLBW rates of live births at five US hospitals, two in West Virginia and three in California, from Jan 2017 to Dec 2020. We compared PTB and VLBW rates in 2020 to 2017–2019 using Poisson regression and rate ratio with a 95% confidence interval. We stratified live births by gestational age (GA)  and birth weight . We examined PTB rates at four of the hospitals during Jan-Dec 2020 and compared them to prior period of Jan 2017–Dec 2019 using Statistical Process Control (SPC) for quarterly data.p = 0.222). There was no difference in the subcategories of gestational age <33 or 33–36 weeks in any of the centers. p = 0.832). There was no difference in the subcategories of birth weight 1,000–1,500 or ≤1,000 g in any of the centers except the regional NICU in San Jose, California showed an increase in ELBW  from 0.4% in 2017–2019 to 0.8% in 2020 (p = 0.013). We examined PTB and VLBW rates in 34,599 consecutive live births born Jan 2017–Dec 2019 to the rates of 9,691 consecutive live births in 2020. In the first year of the pandemic, 2020, we did not find a significant change in the rates of PTB or VLBW when combining the live birth data of five US hospitals in two different states and three different regions. No decrease in PTB or VLBW rates were noted at any of the five hospitals examined.Similar results are noted in a study from France seeking to examine effect of lockdowns in perinatal outcomes in 2020, showing neither differences in preterm, nor stillbirth rates, nor LBW and adjusted VLBW rates in their cohort . In addiThere was a significant increase in ELBWs in the San Jose regional hospital which was likely related to the change in referral patterns of high risk deliveries across the local county rather than the pandemic. The county acquired two community hospitals in early 2019 and the referral pattern for high-risk mothers were streamlined to deliver at the regional center. In addition, there was a local closure of the Labor and Delivery service at another community center that also redirected high risk mothers to the regional center.As detailed above, multiple studies have been performed to analyze the effects of the COVID19 pandemic on perinatal outcomes and have shown conflicting results. A large rapid review and meta-analysis by Vaccaro et. al., was performed to examine the impact of the COVID-19 lockdown on the incidence of preterm birth, low birth weight, and stillbirth during the lockdown measures . When coStrengths of our study include a longer period of observation of all pregnancies into the early COVID era, a robust sample size with geographic, racial and ethnic heterogeneity. Limitations of our observations include lack of stillbirth data and inability to distinguish spontaneous vs. medically indicated preterm birth. Further limitations of our study include completing our study in 2020 thus lacking accurate SARS-CoV-2 infection rates and possibly largely excluding impact of vaccines and differing SARS-CoV-2 variants in pregnant women. In addition, local factors such as changes in referral patterns related to bankruptcies, mergers and acquisitions cannot be excluded.We conclude that in the first COVID-era year studied, 2020, SARS-CoV-2 did not reduce preterm or low birth weight rates in three different regions in the United States when combining the birth data of five US hospitals, compared to live births 2017–2019 at these same institutions. Local, regional and population-wide studies show variation in impact in SARS-CoV-2 on preterm birth rates, due to multitude of dynamic factors. These will have implications for planning and delivery of regionalized perinatal health care systems.


The effects of some environmentalfactors such as genotype, sex, birth type, dam age, weaning age, and birthweight on growth, ultrasound measurements, and carcass traits weresignificant ( Increasing population and industrialization caused an intensedemand for meat production in the country. However, boosting the number ofsheep would not be sufficient for highly priced carcass parts and aprofitable income. Urbanization and diminishing pastures require the use oftechnology in the sheep industry. In recent years, great activities oncrossbreeding including molecular techniques have been emphasized for moreproduction in livestock breeds. Marker-assisted introgression (MAI) may takeplace in the sheep industry to develop the characteristics of meat. Texel isone of the most known muscled sheep breeds. The single nucleotidepolymorphism . Atotal of 103 pure Ramlıç ewes  of lambs were calculated after subtractinggastrointestinal content from pre-slaughter weight (PSW). Commercialdressing percentage (based on pre-slaughter live weight) and real dressingpercentage (based on empty body weight) were calculated. After carcasssplitting in two symmetric parts along the vertebra, cold half carcassweights (CHCWs) were recorded, and the left-half side was jointed into sevenanatomical parts as described by Colomer Rocher et al. (1988): shoulder,flank, leg, neck, anterior rib, loin ribs, and tail. Each joint was weighed.The legs of lambs were dissected to divide muscle, fat, and bone and thenweighed. Internal carcass length (first rib to anterior symphysis pubis),external carcass length (atlas to tail), chest depth (sternum to sixththoracic vertebrae), shoulder width (distance between tubercle of thehumerus), rump width (distance between tubercle of coxae), and leg lengths were determined on the right-half carcass by usinga tape measure .The linear models including fixed effects of genotype, sex, birth type,month of birth, age of dam, weaning age, and weight were used to describethe collected data for growth traits and carcass characteristics by general linear model (GLM)procedure of MINITAB 18 software. All two- and three-way interactions wereexcluded from the models due to insufficient data in subgroups. The Tukeyprocedure was used for multiple comparisons.3Table 1 shows the least-squares means and standard errors of growth andpre-slaughter carcass traits. The effect of genotype was statisticallysignificant  of BCThe mean weaning weights of Ramlıç and BCNo scientific reports were published for body and ultrasound MLDmeasurements of Ramlıç lambs at weaning. These are the first findingsin the breed so far. BCThe slaughter was carried out at the end of the research. The highestcommercial and real dressing percentages were observed in Texel lambs. Thedifferences between Ramlıç and BCThe differences in external and internal carcass lengths for genotypes werefound to be significant (5Based on the results of the present study the effects of factors such asgenotype, sex, birth type, age of dam, weaning age, and birth weight on pre-and post-weaning growth characteristics, body measurements, and ultrasoniccarcass traits are important. These factors should be taken intoconsideration in selection programs. BC


Cystic Echinococcosis or Hydatid disease is caused by the infection with the larval stage of long tapeworm, Echinococcus granulosus. This condition often remains asymptomatic for years before the cyst grows large enough to cause symptoms in affected organs. The most common organs involved are liver and lungs although the heart, brain, bone, central nervous system, and kidney may also be involved. This case is about a young woman who presented with left flank pain and urinary tract infection who was later diagnosed as having left renal hydatid cyst. The cyst was approximately 7.8*6.6*8cm with internal multiple septations at the lower pole cortex of the left kidney. Laparoscopic pericystectomy was performed and with no postoperative complications, she was discharged on albendazole and other supportive medication. With timely management using combination therapy, this condition is curable and the patient can live a healthy life with normal kidney function. The larval stage develops in intermediate hosts including man and adult stage is found in carnivores. Clinical disease results mainly from pressure effects caused by the enlarging cyst several years after initial infection.2 First line treatment is surgical removal. Albendazole  is prescribed before and after surgery. We report a case of 24 years female, presented with abdominal pain and diagnosis of left renal hydatid cyst.CT scan is superior to ultrasonography with higher diagnostic accuracy for diagnosis. Serology and other tests are considered complementary.24-year-old female from Simikot, Humla presented to Patan hospital with left flank pain and feverish feeling for 5 days. Vitals were stable and the only notable finding was tenderness over the left hypochondrium on deep palpation. Urinalysis revealed pus cells with plenty of RBCs without growth on culture. Biochemistry and eosinophil count was normal with slightly increased total count. No hydatiduria was noted in urinalysis.Ultrasonography revealed cystic lesion measuring 7.1cm x 7.9cm in lower pole cortex of left kidney with internal septations suggestive of hydatid cyst (Gharbi Type III) .Contrast enhanced computed tomography scan further supported the findings of USG. CT scan revealed cystic lesion measuring 7.8 x 6.6 x 8.0cm at mid and lower pole cortex of left kidney with notable internal multiple septations. . The posManagement of the UTI was done with follow up for the cyst. DTPA scan was carried out in the next follow which showed a normal sized left kidney with impaired function and delayed non obstructive clearance evidenced by differential function of 36 and GFR 28.8ml/min/1.73sqm.Laparoscopic pericystectomy for the left hydatid cyst was performed. With intraoperative findings of a big hydatid cyst in the inferior pole of the left kidney measuring approximately 8.0 x 7.0 x 8.0cm containing multiple daughter cyst and pus.1Hydatid cyst, the larval stage of Echinococcus, is a bladder-like cyst formed in various organs and tissues following the growth of the oncospheres of an Echninoccus tapeworm in that specific organ or tissue. Oncospores developed from eggs later develop to hydatid cyst (infective stage) in intermediate host including man. Infection is through swallowing eggs from contaminated vegetables or uncovered foods or inhalation of dust with eggs. After ingestion by the intermediate host, the oncosphere penetrates the gut wall and circulates in the blood  to the liver or in the lymph to the lungs (commonest sites) but sometimes escape into the general systemic circulation and develop in other organs and tissues like kidney.3 Renal echinococcosis usually presents between the third and fifth decade of life and consists of 2-3% of all cases.4 It usually remains asymptomatic for many years.5 Symptoms and signs of hydatid cyst disease depend on the involved organ, the site, and the secondary spread. A palpable mass is the most common clinical finding. It may present with lower back pain, hematuria, or an abdominal lump.7 In up to 18 to 20% of cases, the rupture of the cyst into the urinary collecting system causes hydatid urea which is a pathognomonic sign of renal hydatidosis.9A World Health Organization (WHO) study in 2010 estimated the incidence of cystic echinococcosis per 100,000 population in Southeast Asia to be 0.8.10 Radiologic imaging through ultrasonography (US), computed tomography (CT), and magnetic resonance imaging (MRI) proves the diagnosis.9Combination of diagnostic methods is necessary for diagnosis of hydatid cyst in some organs with no symptoms (e.g. kidney) with clinical history, serological analysis, urine analysis and medical imaging. Specific serological tests could be done using immunoglobulin G enzyme-linked immunosorbent assay and immunoelectrophoresis.12 When possible, kidney sparing cyst removal is performed through cystectomy and pericystectomy; however, nephrectomy is needed when the hydatid cyst invades a major renal part or in cases of hydatiduria.9 Albendazole is the antiparasitic agent of choice whether prescribed alone or as adjuvant therapy in two daily doses adding up to 10-15mg/kg/day. Albendazole in cycles of three to six months to treat hydatid disease with liver involvement induces cyst shrinkage and inactivation according to WHO-Informal Working Groups on Echinococcosis (WHO-IWGE) standards based on ultrasound characteristics, although associated with frequent recurrence. Drug therapy for renal hydatid disease is safe but ineffective. Therefore, surgery has been considered the treatment of choice. Albendazole is prescribed as a prophylactic drug at a dosage of 10-15mg/kg/day (maximum of 400 mg) orally before and after surgery.6For treatment of these cases both pharmacotherapy and surgery are needed. Surgery is via open and retroperitoneal approach.In our case, laparoscopic pericystectomy was performed for the left renal hydatid cyst. The kidney was preserved as the major renal parts were not involved. The postoperative period was uneventful. The patient completed five cycles of albendazole therapy and now she is asymptomatic and living a healthy life.Although a rare entity, renal hydatid cyst can be encountered in our setting. It can be found incidentally while evaluating for other common urological conditions like urinary tract infection (UTI). Following diagnosis, management using a combination of surgical and medical treatment can help patient with this condition to continue a disease free and healthy life.


A short fetal femur in prenatal diagnosis might be an indicator for intrauterine growth retardation (IUGR), a genetically determined small child (SGA) with or without associated fetal malformations and/or an adverse fetal outcome.1373 singleton pregnancies with a femoral length < 5th percentile detected between 1999 and 2015 during second-trimester screening in a tertiary prenatal diagnostic center were subjected to a descriptive retrospective analysis with regard to fetal characteristics as well as pregnancy outcome.p < 0.001); in addition, a higher rate of preterm births 36.6% vs. 11.3%, p < 0.001) and SGA babies  were observed in the first collective.685 (49.9%) fetuses presented an isolated short femur, while 688 (50.1%) showed additional abnormalities. 293 (42.6%) of those were SGA babies without any malformation, while 395 (57.4%) had one or more severe anomaly of the following organ systems: 157 (11.5%) cardiovascular, 101 (7.4%) musculoskeletal, 82 (6.0%) urogenital, 72 (5.2%) cerebrocephalic, 50 (3.6%) gastrointestinal, and 5 (0.4%) thoracic. 75 (5.5%) of the fetuses showed chromosomal aberrations of which Trisomy 13, 18 and 21 were found in 2, 13 and 27 of the cases, respectively. Fetuses with associated malformations had a significantly lower live birth rate than those without (64.2% vs. 98.1%, Diagnosis of a short fetal femur should lead to an extended organ screening; in the case of associated abnormalities, additional genetic testing has to be offered, as well as intensified pregnancy monitoring in pregnancies at risk for IUGR and/or preterm birth. While during the first trimester of pregnancy, fetal crown-rump length and biparietal diameter are used to measure and confirm gestational age, a combination of the fetal biparietal diameter, the head circumference, the abdomen diameters and its circumference as well as the femur length of the baby is used during second and third trimester for prenatal growth estimation and weight assessment. Hereby, the measured length of the femur is elementary .In 1994, Snijders and Nicolaides first established percentiles to define normal ranges for biometric parameters. A short fetal femur was defined as a femur length below the 5th percentile .Up to now, the discriminatory power of a prenatally diagnosed “short femur” has been discussed heterogeneously as it may indicate early growth restriction –5, be a Our outpatient practice is highly specialized for prenatal diagnostics with about 15.000 patients a year. A search of the database between 1999 and 2015 was performed to retrieve all cases with a short femur < 5th percentile during second-trimester screening (18 + 0–21 + 6 weeks of gestation) according to the calculation of Verburg et al. [Gestational age was calculated by the first day of the last menstrual cycle and confirmed by crown-rump length during early ultrasound examination before 12 + 0 weeks of pregnancy. If the difference between clinical and ultrasound dates was more than 7 days, gestational age was corrected according to the earliest ultrasound findings.Besides biometric calculations, all fetuses with a short femur underwent detailed anatomic scanning, and karyotyping was offered to all women with additional fetal abnormalities or was performed at patient´s request. After the exclusion of incomplete or implausible data sets as well as compilation of repetitive examinations of a fetus, 1373 cases were subjected to a more detailed analysis. The assessments were performed by five highly specialized (DEGUM II-III) sonographers with the most modern ultrasound systems of the time (list at the authors). Data on prenatal findings as well as perinatal outcome were collected.p < 0.001), these variables were described using median and interquartile ranges. Statistical comparisons between groups with regard to metric variables were performed by the non-parametric Mann–Whitney U test. All statistical tests reported here were two-tailed, and a p value ≤ 0.05 was considered statistically significant. The study has been granted an exemption from requiring ethics approval by the local ethics committee of Ulm University.Statistical analysis was performed using SPSS Statistics version 21 software . Data on categorical variables were summarized using absolute and relative frequencies, and associations between categorical variables were analyzed with the Chi-squared test or Fisher’s exact test (in case of expected frequencies of less than 5 in 2 × 2 contingency tables). As the distribution of all quantitative (metric) variables included in this study differed significantly from normal  were Caucasian.Median age of women was 31 years (range 15–49), 97% of women of whom data of ethnicity was recorded .Among the 1373 fetuses with a short femur, 1213 (88.3%) live-born children were documented, while 28 (2%) perinatal deaths, 64 (4.7%) miscarriages or intrauterine demises and 68 (5%) voluntary terminations, of whom nearly 90% were performed before 24th week of gestation, were registered.n = 286) had a birth weight below 2500 g.Birth weight was documented for 1241 (90.4%) of the live births. Median birth weight was 3050 g (200–5090 g), while 23.0% (n = 416). The total percentage of preterm infants was 15.5% (n = 188), of which 30% were very preterm infants born before 32 + 0 weeks of gestational age.The proportion of SGA children of the 1213 live births was 34.3% (p < 0.001).A differentiated analysis of the association between preterm birth and SGA revealed that SGA fetuses were born significantly more were born more often as premature babies than non-SGA fetuses (n = 685) presented with an isolated short femur, the other half  showed additional abnormalities: 293 (42.6%) of those were SGA babies, while 395 (57.4%) had one (n = 211) or more severe malformation.While half of the fetuses  cardiovascular, 101 (7.4%) musculoskeletal, 82 (6.0%) urogenital, 72 (5.2%) cerebrocephalic, 50 (3.6%) gastrointestinal, and 5 (0.4%) thoracic  malformations were encoded postnatally.n = 61) carried additional malformations besides a short femur and their mothers were slightly older (33 years vs. 31 years) than the average age of the cohort.75 (5.5%) of the fetuses showed chromosomal aberrations. In 14 cases, structural aberrations were found, while 61 fetuses presented with numeric aberrations of which Trisomy 21, 18, 13, Monosomy X  or Triploidy were found in 27, 13, 2, 10 and 6 cases, respectively. The majority of these fetuses ; children with an isolated short femur had a higher live birth rate (97.8% vs. 78.9%) and a lower rate of perinatal death (0.1% vs. 3.9%), abortions (0.3% vs. 9.6%) or spontaneous miscarriages/intrauterine demises (1.8% vs. 7.6%)  . Within this group, 5.3% children with isolated shortened femur were born preterm vs. 25.4% of fetuses with additional abnormalities and/or SGA (p < 0.001).The results of the present study showed an overall increased preterm birth rate of 15.4% (As a descriptive retrospective study, the presented analysis has its limits: the value of our data reflects that of a middle European population being recruited by a single center and reflecting the referral base of the institution. Aneuploidy rate and outcome variables are those of fetuses seen for organ screening in the second trimester. Due to the fact, that underlying chromosomal anomalies as, e.g., trisomy 13 or 18 tend to be limited at earlier gestational ages, the overall rate of associated anomalies and chromosomal disorders in short femur cases might be even higher.Due to the non-centered medical care in a large metropolitan area with a high number of birth centers, it was nearly impossible over the time period of 16 years to obtain a standardized follow-up of the neonates in retrospective, certainly a shortcoming of the study, as so often in retrospective analyses.A major strength of the study is the large sample size with an overall number of 1373 fetuses with a short femur including 685 cases with an isolated femur < 5th percentile. To the best of our knowledge, so far, the presented work is the largest single study to evaluate short femur in prenatal ultrasound examination and its correlation to fetal outcome.In summary, within 1373 fetuses analyzed, a femur below the 5th percentile in 49.9% of cases was an isolated symptom.Besides 14 cases of miscarriage/abortion, the later were born as otherwise healthy newborns. Though intense sonographic surveillance is not needed in the later, it will most likely be performed by suspicion of evolving complications, uncertainty of a sonographer or need for safety by the mother-to-be.28.7% of the fetuses showed additional abnormalities, including 23.2% structural malformations and 5.5% chromosomal abnormalities. The present study emphasizes that a qualified ultrasound during pregnancy is not only able to perform fetal weight estimation but also might detect, if irregular, possible associated fetal problems.In any case of additional structural abnormality, genetic testing should be offered. If a short femur is found to be part of a small for gestational age baby, an intrauterine growth retardation or a suspected late growth retardation, standard pregnancy care should be intensified by additional intervallic biometry and Doppler assessment on a 2 weeks basis.In the absence of studies combining prenatal investigations with prospectively planned postnatal follow-up, the real impact of a short fetal femur on perinatal outcome and especially long-term development of affected children remains unclear. Further data collection and prospective studies are warranted to determine best prenatal care of unborn with short femur.


Heterodera schachtii is a plant pest responsible for crop loss on a global scale. Here, we introduce a high-throughput system based on computer vision that allows quantifying beet cyst nematode infestation and measuring phenotypic traits of cysts. After recording microscopic images of soil sample extracts in a standardized setting, an instance segmentation algorithm serves to detect nematode cysts in these images. In an evaluation using both ground truth samples with known cyst numbers and manually annotated images, the computer vision approach produced accurate nematode cyst counts, as well as accurate cyst segmentations. Based on such segmentations, cyst features could be computed that served to reveal phenotypical differences between nematode populations in different soils and in populations observed before and after the sugar beet planting period. The computer vision approach enables not only fast and precise cyst counting, but also phenotyping of cyst features under different conditions, providing the basis for high-throughput applications in agriculture and plant breeding research. Source code and annotated image data sets are freely available for scientific use.The beet cyst nematode  Heterodera schachtii, are parasitic on plants and responsible for losses in crop yield that amount to annual financial losses of more than 150 billion USD  Cyst nematodes persist in the soil over many years as eggs inside a cyst, which is a protective shell formed by the remains of the former female body. Once a host plant germinates, juveniles are stimulated to hatch from the eggs and to leave the cyst. Juveniles move through the soil matrix, penetrate the roots and induce a so-called syncytium in the root tissue, causing damage to the plant. A female nematode can produce several hundred eggs. Once the eggs are developed, the female nematode dies and its body wall hardens, forming a brown, sclerotized cyst.The ability to quantify nematode infestation is a prerequisite for control measures targeted at nematodes, as well as for the development of nematode-resistant plant breeding lines. For example, nematode population density may be estimated by cyst counting: The cysts need to be hand-picked from soil samples that still contain organic debris, where the amount of debris depends on the particular sample extraction method . An extensive evaluation shows that our system is able to detect and segment nematode cysts with high accuracy (Sections 3.1–3.3). In addition, we present a use case for cyst phenotyping, demonstrating how cyst populations can be characterized morphologically (Section 3.4).In this work, we introduce a computer vision based system for high-throughput phenotyping of nematode cysts in extracts from soil samples. This is a challenging scenario with target cysts immersed in a large number of distractors, such as soil particles and plant seeds. For the purpose of model training and evaluation, we created three image data sets with annotated segmentation masks or counts of cysts: Heterodera schachtii. Samples came from different field plots (1.8 × 4.5 m) distributed in a traditional sugar beet growing area in the Rhineland (Western Germany) and from an experimental field at the Julius Kühn-Institute field station in Elsdorf, also situated in the same growing region . The samples were compiled by mixing and homogenizing soils from 10 different plots with a comparable population density, either from the top soil (0–30 cm) or the sub soil (31–60 cm). Using the semi-automatic soil sampler system NH120 , 12 evenly distributed soil cores were taken from each plot.Soil samples were taken from fields with a known infestation history of the beet cyst nematode 4 solution (1.26 g/ml) to facilitate buoyancy of the organic fraction. For each sample, 100 or 300 g of field fresh soil were processed, resulting in a soil extract containing a number of cysts ranging from zero to several hundreds (Soil extracts for image acquisition were prepared using a sieve combination (2 mm and 100 μm). Subsequently, cysts were extracted from sieve residues by the centrifugation flotation technique  with a PhenoAIxpert HM prototype  providedIn the PhenoAIxpert HM prototype, the sample stage consists of a sample holder that can be moved manually to predefined positions, such that the complete surface of the sample can be imaged in a series of photographs. Image acquisition is triggered manually with a control computer. In the future, the sample stage will be replaced by a motorized X-Y table, which will then automatically move the sample to predefined positions and trigger image acquisition in a synchronized manner.In order to validate the cyst detection and segmentation performance of the automated system, i.e., image recording followed by the computer vision pipeline, we created three image data sets and associated evaluation scenarios:Cyst-Segmentation: Segmentation scenario. Cyst boundaries were manually annotated on the images. The data set consists of 229 images with a total of 6, 331 annotated cysts. We randomly split the data into the 80% training set  and the 20% test set . The training set was used for training of all models in this work, and the test set served for evaluation of segmentation accuracy (Section 3.3). Considering that cysts surrounded by debris particles can easily be overlooked, two raters labeled the data independently with the polygon tool in the annotation software IMANNO developed by LfB (publicly available). The two sets of annotations were then manually merged and validated by a nematologist (co-author Matthias Daub).Cyst-Count  in a semantic segmentation setting, performing pixel-level classification of “cyst vs. non-cyst,” as well as “boundary vs. non-background.”As the images enclose a large field of view, they had to be processed patch-wise in a split&stitch manner due to GPU memory limitations. A semantic segmentation map is simpler and more efficient to stitch than the instance label map generated by detection-based approaches such as Mask-RCNN  = 1/(1+exp(−x)).Both convolutional layers, Model training. We trained the model on the images from the training set of the Cyst-Segmentation data and then applied it to the test set, as well as to the other data sets from the counting scenarios  and random gamma transformation  were performed.ce = −p2log(p1)−(1−p2)log(1−p1), both for cyst prediction and for boundary prediction:The model was trained with the standard binary cross-entropy loss CE computes the mean value of ce applied element-wise to every pixel. C and B are the binary maps for “cyst” and “boundary,” respectively. To obtain the boundary map B, we performed a morphological dilation and erosion on the label map, using a disk-shaped structuring element of radius 2. The difference between the dilated and eroded label map was taken as the boundary training map.The function We trained the models for about 550 k iterations with the batch size of 4, using the RMSprop optimizer . Each patch was processed separately by the trained CNN model, obtaining a cyst score map and a boundary score map.Stitching: Patch predictions were stitched back to the original positions in the image. In the region of overlap, we averaged over the cyst scores from different patches, whereas for the boundary score, we took the maximum. This was motivated by the fact that the boundary is the more vulnerable structure and that successful separation of adjacent cysts relies on a continuous boundary segment between them. For stitching, we also removed the outermost 64 pixels that contain more erroneous predictions due to partial visibility of cysts.Instance segmentation. Finally, we obtained an instance segmentation from the image-level semantic segmentation.Instance separation: We employed a threshold of 0.5 to distinguish cyst pixels from background pixels in the cyst map. In the same way, boundary pixels were obtained from the score map. By excluding cyst pixels that were also boundary pixels, cyst instances could be separated spatially. Instances on the binary, thresholded map were then uniquely labeled by connected components labeling (8-neighbor connection).Post-processing: Cyst pixels not assigned to any cyst instance were merged to the closest one. Furthermore, noisy predictions were removed based on a safe minimal size (500 pixels).Cyst-Segmentation, we matched each predicted object to the ground truth object with the largest intersection over union (IoU). Using a certain IoU threshold, a match can be considered as a success (threshold exceeded) or a fail. The successful matches define the True Positives (TP), while predicted objects that have no successful match are False Positives (FP), and ground truth objects without successful matches are False Negatives (FN). Accumulating these values through images of the whole data set, a precision metric (P) can be defined as:For the fully annotated data set By sweeping over a range of IoU thresholds, an average precision under different levels of matching rigor can be computed as an overall metric, taking into account the effect of segmentation accuracy:where the threshold values range from 0.5 to 0.95 with a step size of 0.05 in this work. Furthermore, we computed the positive predictive value (PPV) to quantify detection accuracy, and the False Negative Rate (FNR) to measure how many objects were missed:We also report the Average PPV (APPV) and the Average FNR (AFNR) over different IoU matching thresholds. As a further measure for segmentation quality, we used the Aggregated Jaccard Index (AJI)  and had either low or high cyst density (Section 2.3). For all combinations of these categories , we considered samples with cysts and organic debris particles (“debris”), as well as clean samples (“clean”) where the cysts had been manually separated from the debris particles.In the Across all soil sample types, manual cysts counts were similar to automatic cyst counts, both for the “debris” and for the “clean” case . PearsonThe samples contained between about 50 and about 300 cysts. Fitting a linear regression model to the manual (x) vs. automatic (y) cyst count results revealed a small y offset of only ≈+3 for the automatic cyst count on the clean samples, while the y offset was ≈+22 for the debris samples . Hence, Cyst-Count may still be affected by False Positive (FP) detections, as debris particles can resemble the cysts  than for the 100 g samples with the same number of cysts  1,394 cysts immersed in debris particles had to be segmented. The manually segmented ground truth was provided by a nematologist, while the automatic segmentation was performed with the computer vision pipeline that relies on the U-Net as a backbone network (Methods). For comparison, we also employed two alternative network architectures, ResNet50 and ResNet101 (Methods).We next evaluated the accuracy of the nematode cyst segmentations. For the Due to the variable number of cysts per image, we chose an evaluation setting based on cumulative scores, i.e., all cysts from all images were pooled. We compute the False Negative Rate  and Positive Predictive Value (PPV) at IoU thresholds τ = 0.5, …, 1.0 for each of the three network architectures .The same trend could be observed for all three network architectures : For IoUThe Positive Predictive Value , that measures the amount of all true positives among all true and false positives, exhibited the opposite trend, being constantly high above 0.8 up to τ≈0.8 and decreasing for higher τ, crossing the FNR line at τ≈0.9.While the results were similar for all network architectures, the U-Net had slightly, but consistently, lower FNRs (and slightly higher PPVs) for τ up to about 0.9.In In summary, we observed only minor performance differences for the alternative networks, indicating that network architecture is not a critical part in the computer vision pipeline as long as a reasonable choice is made. Overall, the computer vision pipeline with the default backbone network yielded accurate instance segmentations with about 80% of the cysts being segmented with a good IoU of about 0.8 or higher. For qualitative results, see the segmentation examples with annotated IoU scores in Cyst-Count data (clean version without debris particles) and then computed two morphological features, cyst size (in pixels) and length-width ratio, to describe the phenotype of a cyst. The Cyst-Count data (time_point with levels “before season” and “after season”). The samples were extracted either from “top soil” or from “sub soil” (factor soil_type). As we observed no interaction between the factors time_point and soil_type  and in terms of the length-width ratio .Inspecting the feature distributions, we discovered clearly visible differences between the cyst populations from before and after the planting season . The difThe feature distribution for cyst size was flatter for the “after season” samples than for the “before season” samples, with less small and more large cysts in the samples that were taken after the planting season . The avep = 0.0003168), but not for the length-width ratio .For the cyst populations extracted from different soil types, the feature distributions appeared more similar . The aveCyst-Count data (clean version without debris) show how even subtle phenotypical differences become visible in a population level quantitative analysis that is greatly facilitated by an automatic phenotyping system that can annotate large image data sets in a consistent manner. We conducted the population-level study on the cleaned data set in order to explicitly analyze the biological effects, unaffected by FP detections due to debris particles.The above results on the Cyst-Count data with debris particles (p = 2.870 × 10−6) and length-width ratio  between the populations from before and after the planting season (p = 0.05229), while the difference between the length-width ratio distributions remained non-significant , exactly as for the data without debris particles.We then repeated the same analysis for the articles . While tg season . Regardig season , a smallCyst-Segmentation data (test set) with a ground truth of 1394 manually annotated cyst masks .Finally, we also verified that the feature distributions based on automatic segmentations were sufficiently similar to the feature distributions based on manual segmentations . The anap = 0.6953), as well as the length-width distributions .Both average size  and average length-width ratio  were similar for the manually and the automatically segmented cysts. The size difference of only about three pixels is negligible for cyst sizes of several thousand pixels, while the 0.03 difference in length-width ratio amounts to 25% of the 0.12 length-width ratio difference observed in We conclude that the differences between automatic and manual segmentations that exist on the single-cyst level cp.  are of mWe have introduced a high-throughput phenotyping system for nematode cysts in extracts from soil samples (Methods). At the core of the automated system lies a computer vision pipeline that achieves robust and accurate instance segmentation of nematode cysts in cluttered object collections with many remaining debris particles that often resemble the cysts in terms of shape and color .We have validated the automated system on a large number of images of soil sample extracts from different soils, comparing its results both to manual cyst counts and to manually annotated cyst segmentation masks (Results and Discussion). Manual and automatic cyst counts were highly correlated .The large number of debris particles in the soil sample extracts did cause FP cyst detections. Based on an exact ground truth in the form of artificial soil samples, we could establish that the amount of FPs does not depend on the true number of cysts, but only on the sample weight, i.e., FP detections cause a count offset that is approximately constant (for a given sample weight) and that is only relevant for very small cyst numbers .Inaccuracies on a cyst-level had no influence on the population-level phenotypical statistics: The feature distributions for automatically segmented cysts were not significantly different from those of manually segmented cysts .Cyst size and length-width ratio are simple phenotypical features that can be computed from segmentation masks, and we have used them as a proof of concept for automatic characterization of the differences between nematode cyst populations in samples taken before and after the sugar beet planting season, as well as in samples taken from top and from sub soil. Further research is required to determine whether the observed differences between these cyst populations generalize to soils from other locations.To date, large-scale phenotypical studies on huge, multi-national data sets are infeasible in practice due to intra-rater variability for the manual segmentations and the amount of work involved, and due to differences in experimental setups. With an automated high-throughput phenotyping system, as we have presented it in this work, it becomes possible to conduct large-scale analyses of soil samples from around the world in a reproducible way with a standardized setup and with standardized image analysis routines.Already cyst size is a morphological feature of high relevance for monitoring nematode resistance . The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.Authors LC, MS, and DM are employed by RWTH Aachen University, Aachen, Germany. Author MD is employed by the Federal Research Center for Cultivated Plants (Julius Ku8hn Institute), Elsdorf, Germany. Authors H-GL and MJ are employed by LemnaTec GmbH, Aachen, Germany. The image recording system used in this work, PhenoAIxpert HM, is a prototype that will be rolled out as commercial product by LemnaTec GmbH. The reviewer RH declared a shared affiliation with the author MD to the handling editor at the time of review.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The human claustrum is a gray matter structure in the white matter between insula and striatum. Previous analysis found altered claustrum microstructure in very preterm-born adults associated with lower cognitive performance. As the claustrum development is related to hypoxia–ischemia sensitive transient cell populations being at-risk in premature birth, we hypothesized that claustrum structure is already altered in preterm-born neonates.developing Human Connectome Project.We studied anatomical and diffusion-weighted MRIs of 83 preterm- and 83 term-born neonates at term-equivalent age. Additionally, claustrum development was analyzed both in a spectrum of 377 term-born neonates and longitudinally in 53 preterm-born subjects. Data was provided by the increased absolute and relative claustrum volumes, both indicating increased cellular and/or extracellular matter and being in contrast to other subcortical gray matter regions of decreased volumes such as thalamus; (ii) lower claustrum FA and higher claustrum MD, pointing at increased extracellular matrix and impaired axonal integrity; and (iii) aberrant covariance between claustrum FA and MD, respectively, and that of distributed gray matter regions, hinting at relatively altered claustrum microstructure.Claustrum development showed increasing volume, increasing fractional anisotropy (FA), and decreasing mean diffusivity (MD) around term both across term- and preterm-born neonates. Relative to term-born ones, preterm-born neonates had (i) Results together demonstrate specifically aberrant claustrum structure in preterm-born neonates, suggesting altered claustrum development in prematurity, potentially relevant for later cognitive performance. Beyond dulthood . As in tdulthood , we suggbasic forebrain functioning  by comparing claustrum macrostructure, microstructure, and structural relationship with other brain regions. As this is the first analysis of the human neonatal claustrum, the study provides contextual background about the claustrum development around term: first, we evaluated the claustrum structure across the spectrum of 377 term-born neonates of the dHCP around term; second, preterm claustrum development was examined by a longitudinal evaluation of 53 preterm-born neonates of the dHCP who passed a follow-up scan in the range of GA 38 to 45. To perform all analyses of the claustrum, we used a previously published deep learning algorithm to segment the neonate claustrum  (https://github.com/BioMedIA/dHCP-release-notes). We evaluated the following samples: first, to provide context information about claustrum development, the available spectrum of term-born subjects, i.e., 377 neonates  A. Recruime.org/) . Writtenme.org/)  and the me.org/) . The sesme.org/) . Scans wneonates B, and a neonates C, were aneonates D. To reaStructural MRI: Due to immature brain appearance in neonates, the preferred structural image sequence in such a young population is T2-weighted MRI   . All sca0.5 mm3) , bias co0.5 mm3) . Further0.5 mm3)  and it w0.5 mm3) . We usedDiffusion-weighted MRI: Diffusion-weighted images (DWI) were acquired with a monopolar spin-echo echo-planar imaging Stejksal-Tanner sequence based on multi-shell High Angular Resolution Diffusion Imaging  ( 1.5 mm) . Diffusi 1.5 mm) . Mean di 1.5 mm)  based on 1.5 mm) . DWI com 1.5 mm) .2.2https://github.com/hongweilibran/claustrum_multi_view).To analyze the claustrum in newborn subjects, we applied three-dimensional segmentations in T2-weighted structural MR images . As manuwww.itksnap.org)   to ensurnap.org) . Additioclaustrum macrostructure ), and claustrum microstructure . Analyses were performed with the mean of right and left claustrum for an overview, and for right and left claustrum separately to identify possible differences.The claustrum segmentations were used for structure outcome definition, namely 2.3The relationship of regional cortical and subcortical gray and white matter structure with claustrum structure was analyzed by structural covariance analysis . The Dra2.4Statistical analyses were performed with R (version 3.6.3). Thresholds for statistical significance were set to p < 0.05.context information about claustrum development around birth, we studied first, how claustrum structure depends on age around birth for a spectrum of term-born subjects. Therefore, we analyzed all term-born subjects with scans from GA 38 to 45 using a general linear model approach (GLM). Absolute claustrum volume, TBV-relative claustrum volume, MD, and FA were dependent variables, respectively. Scan age was the independent variable, and the model was corrected for sex and birth age. Second, concerning claustrum development of preterm-born subjects around birth, we examined longitudinal MRIs of the dHCP in preterm-born neonates. Paired t-tests were applied to compare first and second scans of preterm-born subjects scanned twice for a longitudinal study of the same variables, namely absolute claustrum volume, TBV-relative claustrum volume, MD, and FA. Subjects who were scanned twice were demographically compared with subjects scanned once to determine a possible bias. Complementary, all available scans of preterm-born neonates were included in linear mixed model analyses with the same metrics as mentioned above as dependent variables, respectively, and scan age as independent variable. The models were controlled for birth age, sex, and random intercepts for individual subjects.To provide main analysis of the effect of preterm birth on neonate claustrum structure, we tested for significant differences between preterm- and term-born neonates at term-equivalent scan age, to determine whether preterm birth influences relative or absolute claustrum volume, MD, and FA. We included all preterm-born newborns, who were scanned in the scan age range of term-born neonates (GA 37.4–44.9), and just as many term-born neonates with minimal scan age gap to gain term-equivalent conditions. A GLM approach was performed for absolute and TBV-relative claustrum volume, MD, and FA, respectively, controlled for sex and scan age.For our Control analyses: To assess if the selection of control subjects had an impact on the results, the term-born neonates who were included as controls and the subjects who were not included were compared regarding their demographic characteristics. Furthermore, the GLM approach was repeated with all available term-born subjects as controls for the main analyses. Besides, to control the macrostructure analysis, the GLM analyses were conducted but instead of claustrum outcomes, values from other subcortical regions were selected as the dependent variable, namely TBV-relative thalamus and caudate nucleus volumes, to evaluate the specificity of claustrum volume changes. For microstructure analysis, the GLM analyses were repeated with claustrum-controlled segmentations  (https://github.com/cor2ni/3D_brain_plot).Concerning nap.org)  and the 33.12 = 0.11), indicating continuous volume increase of the claustrum around birth. Regarding right and left claustrum separately, both claustra showed a significant volume increase with older scan age with a superior growth rate in the right claustrum   from 38 to 45 weeks . Claustrum macrostructure, i.e., the average absolute volume of both hemispheres, was determined using automated claustrum segmentation, and the results were plotted along scan age A. The so< 0.001) A.Fig. 2C2 = 0.15)   in term-born neonates, we used the same GLM approach as for absolute claustrum volume above but now with claustrum volume relative to TBV  as the dependent variable. The relative claustrum volume decreased significantly around term  B. The re< 0.001) B. As the-3 mm2/s) was the dependent variable, scan age was the independent variable, and the model was corrected for sex and birth age. There was a significant negative association between scan age and averaged claustrum MD , as well as right and left claustrum MD, respectively,  (2 = 0.44), supporting the scan age-dependent decrease of claustrum MD in term-born neonates independent of GM MD changes.To assess changes in claustrum microstructure in term-born neonates around birth, we derived averaged claustrum mean diffusivity, MD, of DWI scans from term-born neonates  across GA 38 to 45 and plotted these values C. Again,< 0.001) C. These 2 = 0.26). This correlation was also seen for right and left claustrum FA separately with a slightly superior increase in the left claustrum  (2 = 0.37), supporting the scan age-dependent increase of claustrum FA in term-born neonates independent of GM FA changes.To further study changes in claustrum microstructure in term-born neonates around birth, we derived averaged claustrum FA from DWI scans of the same subjects as for the MD analysis and plotted them across scan age D. Then, < 0.001) D. These 3.2preterm-born neonates. Therefore, scans at birth and a few weeks after birth were assessed, spanning a period from GA 30 to 45. To estimate the development of claustrum macrostructure, the averaged claustrum volume derived from two longitudinal T2-weighted scans was plotted . A linear mixed model approach including all available 128 preterm-born neonates, controlled for birth age, sex, and random intercepts for individual subjects, confirmed the claustrum volume increase with older scan age , indicating substantial postnatal claustrum growth after preterm birth. Larger claustrum volume with increasing scan age was also observed in right and left claustrum separately in paired t-tests   , respectt-test and linear mixed model approach was applied. Still, the absolute claustrum volume was replaced with relative claustrum volume . We found that the relative claustrum volume significantly decreased around term    B, indica< 0.001) B, but re< 0.001) B and in < 0.001) , respectt-test for first and second scan of 45 subjects scanned twice as performed for claustrum volume increase. There was a significant reduction in claustrum MD . Additionally, linear mixed models were conducted with all available scans of 119 preterm-born neonates correcting for birth age, sex, and random intercept for individual subjects. Again, there was a significant decrease in claustrum MD , indicating microstructural claustrum development after preterm birth. There was little difference between right and left claustrum MD in the paired t-tests  . This result was affirmed by the linear mixed model , indicating postnatal claustrum microstructural development after preterm birth. The claustrum FA increase was slightly superior in the left claustrum than in the right regarding the paired t-test   . The samd t-test D and the< 0.001) .3.33.3.13, FT: 332 ± 51 mm3; p < 0.001; partial η2 = 0.07). While the right claustrum volume was larger than the left claustrum in preterm- and term-born neonates, respectively, the preterm-born claustra were both larger than the term-born claustra, respectively,   and 83 term-born  neonates, spanning GA 37.4 to 44.9. Birth age and birth weight of the 83 term-born neonates were comparable with the data of the other term-born subjects of the dHCP cohort making a selection bias less likely . To exam< 0.001) A. This f2 = 0.03), indicating that claustrum volumes are larger after preterm birth for term-equivalent comparison independent of TBV volumes. While the TBV-relative right claustrum volume , the TBV-relative left claustrum volume was significantly increased in preterm-born neonates (pleft = 0.006)  , suggest2 = 0.07) and significantly smaller relative caudate nucleus volume  in preterm-born neonates. Both results indicate the specificity of larger term-equivalent claustrum volume after preterm birth.When all available 377 term-born neonates were included as control subjects, both findings were reproducible, increased absolute claustrum volume (p < 0.001) and increased TBV-relative claustrum volume (p < 0.001) after preterm birth , confirm3.3.1.1-3 mm2/s, FT: 1.08 ± 0.04 *10-3 mm2/s; p = 0.004; partial η2 = 0.06). Right and left claustrum MD separately showed little differences and were both significantly increased after preterm birth   C. The in= 0.002) , supportTo test how the mean claustrum MD change was related to general GM MD change, we performed the same GLM analysis as just described but included GM MD  as an additional covariate. We did not find a significant group difference for claustrum MD (p = 0.255), indicating that claustrum MD changes are dependent on general GM MD changes after preterm birth.2 = 0.11) . We repeated the GLM analysis with claustrum-controlled MD as the dependent variable, and sex and scan age as independent variables. The claustrum-controlled MD was significantly increased in preterm-born neonates  . Adding 2 = 0.21). Right and left claustrum FA separately were also significantly decreased after preterm birth  with a stronger effect in the right claustrum , indicating that lower claustrum FA does not depend on GM FA changes.2 = 0.31), when correcting for lentiform nucleus  FA , and when correcting for white matter claustrum frame FA .FA values are particularly influenced by fibers, i.e., connections of, to, and from a region. To control whether lower claustrum FA might be confounded by FA of nearby gray or white matter structures, several covariates were added next to scan age and sex in above mentioned GLM approach on claustrum FA, respectively: There was still a significantly lower claustrum FA in preterm-born neonates when correcting for insular cortex FA   , support3.4To analyze the impact of prematurity on the relationship of regional brain structure with claustrum structure, structural covariance analysis was performed for brain regions covering the whole brain. For each metric, i.e., absolute and TBV-relative volume, MD, and FA, respectively, each brain region was correlated with the corresponding measure of right and left claustrum across subjects of both groups, respectively . The calRegarding the absolute volume covariance, there was no significant difference between preterm- and term-born newborns for any brain region. All covariance z-scores, for cortical and subcortical GM, and WM, were positive with the highest mean (±standard deviation) correlation with subcortical GM (PT: 0.57 (±0.18), FT: 0.59 (±0.09)). Claustrum volume covariance with cortical GM was relatively uniform in preterm- and term-born subjects across the cortex (PT: 0.44 (±0.10), FT: 0.43 (±0.10)). These findings indicate a growing trend around term in the whole brain without a specific impact of preterm birth on the regional relationship with claustrum macrostructure.Concerning the TBV-relative volume covariance, there was also no significant difference between preterm- and term-born neonates. We found negative and positive correlations in all tissue types, respectively. However, most cortical GM regions, especially the parietal cortex, showed negative z-scores and the mean coefficient was negative in preterm- and term-born subjects (PT: −0.15 (±0.20), FT: −0.17 (±0.14)). These findings indicate that cortical regions and claustrum show reverse TBV-relative volume trajectories around term without a significant impact by preterm birth.Regarding MD covariance, the correlation of the claustrum MD with MD of the frontal, partly parietal, and temporal lobe, and cingulate gyrus, gray and white matter, subcortical GM like the amygdala, caudate nucleus, and subthalamic nucleus was significantly decreased after preterm birth. There were more significant differences for the right claustrum (23 regions) than the left (11 regions) . The claConcerning FA covariance, the correlation between the right or left claustrum and parts of the parietal, temporal, occipital, parahippocampal lobe, hippocampus, and insula gray and white matter as well as the amygdala, subthalamic nucleus, and lentiform nucleus were significantly increased after preterm birth. Again, there were more significant differences noted in the right claustrum (33 regions including the brainstem) than in the left (10 regions) . While t4Using term-equivalent T2-weighted anatomical and diffusion-weighted MRI scans of preterm- and full-term born neonates, we describe for the first time altered claustrum structure in preterm-born neonates. In particular, claustrum volume was increased after preterm birth, while volumes of other subcortical structures such as the thalamus or caudate nucleus were decreased. Furthermore, claustrum FA was decreased, and MD was increased after prematurity. While larger claustrum volumes indicate an increase of cellular and/or extracellular matter, lower fractional anisotropy and higher mean diffusivity suggest increased extracellular matrix and impaired axonal integrity. Performing a covariance analysis of the claustra with cortical and subcortical gray and white matter, aberrant microstructural covariance in cortical and subcortical regions hints at altered claustrum microstructure relative to that of widespread gray matter (GM). Taken together, these results indicate early alteration of the claustrum structure in prematurity. Data suggest impaired claustrum development around birth, speculatively due to overlapping vulnerability of both, subplate neuron and pre-oligodendrocyte pathways, which are critical for claustrum development. Furthermore, on the functional level, these results may indicate clinical implications for claustrum functions, namely attention and slow wave sleep processes.4.1higher claustrum volumes in preterm-born neonates compared with term-born ones. The observed differences were dominated by the left claustrum. However, the absolute right claustrum volume was also significantly increased after preterm birth. The volume differences between the right and left claustrum are in line with findings in adults in that the deviations vary between studies  which can provide additional details about intra- and extra-neurite volume fraction . Recent To start with the comprehensive interpretation of the claustrum values collected so far, MD increase has been linked to lower cellularity . This caAn inflammatory reaction may have caused an accumulation of the extracellular matrix. Secondary neuroinflammatory processes typically emerge as a response to impaired pre-OL development in prematurity induced by hypoxic-ischemic events . FurtherHowever, we found a volume decrease in the likewise highly connected thalamus. One difference between the two structures is the cellular composition, e.g., the direct subplate neuron involvement in the immature claustrum . It is s4.5The claustrum is suggested to play a role in both attention modulation during wakefulness and slow wave generation during Non-REM sleep . These fAdditionally, the duration of slow wave sleep after preterm birth might be related to claustrum alteration. A previous study found delayed slow wave sleep development in aggrieved neonates . Both, iFurther work is required to test the viability of the claustrum as a prognostic marker of prematurity, possibly together with sleep assessment. One supporting point of these ideas is the specific volume increase of the claustrum in contrast to other GM subcortical regions.4.6This study is based on a latest, public, large-scale dataset with a high image resolution and visual quality control (dHCP). However, some limitations should be taken into consideration. First, the claustrum is a delicate structure, so the segmentation is partly subjective despite the high image resolution. We performed automated segmentation to lower the impact of subjectivity in segmentation. Second, partial volume effects lead to a mixture of gray and white matter and might confound results. This might be a pronounced problem for diffusion space co-registration with limited resolution. To approach this issue, we controlled for surrounding gray and white matter in all analyses and applied a claustrum-controlled segmentation to further reduce the effect of boundary regions in microstructural evaluation. Third, the analysis of GM microstructure in diffusion-weighted images is confined by the applied metrics. Particularly, the FA values are relatively low (between 0.10 and 0.28), and interpretation is ambiguous in this range, at least in the white matter below 0.2 . Additio5We demonstrated increased claustrum volume, aberrant claustrum microstructure, and altered microstructural covariance with the claustrum in preterm-born neonates in comparison with age-equivalent term-born controls. These findings indicate specifically altered claustrum structure in preterm-born neonates, suggesting an impaired claustrum development that potentially reflects the overlapping vulnerability of both subplate neuron and pre-oligodendrocyte pathways in the claustrum.6https://www.developingconnectome.org/data-release/second-data-release/information-registration-and-download/.All MRI data used in this study is part of a public data set of the dHCP. Feel free to register here for data access: https://github.com/hongweilibran/claustrum_multi_view.The code for automated claustrum segmentation in neonatal brain MRI is available on GitHub: https://github.com/cor2ni/3D_brain_plot.The code for 3D brain plots is published on GitHub: This work was supported by the Deutsche Forschungsgemeinschaft (SO 1336/1-1 to C.S.), German Federal Ministry of Education and Research , the RECAP preterm project, an EU Horizon 2020 study , and the Kommission für Klinische Forschung, Technische Universität München . Hongwei Bran Li is supported by Forschungskredit (Grant NO. FK-21-125) from University of Zurich.Antonia Neubauer: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Aurore Menegaux: Methodology, Software, Investigation, Writing – review & editing. Jil Wendt: Methodology, Validation, Writing – review & editing. Hongwei Bran Li: Methodology, Software, Investigation, Writing – review & editing, Funding acquisition. Benita Schmitz-Koep: Investigation, Writing – review & editing, Funding acquisition. Tobias Ruzok: Investigation, Writing – review & editing. Melissa Thalhammer: Investigation, Writing – review & editing. David Schinz: Methodology, Investigation, Writing – review & editing. Peter Bartmann: Investigation, Writing – review & editing, Funding acquisition. Dieter Wolke: Investigation, Writing – review & editing, Funding acquisition. Josef Priller: Methodology, Investigation, Writing – review & editing. Claus Zimmer: Methodology, Investigation, Writing – review & editing. Daniel Rueckert: Methodology, Investigation, Writing – review & editing. Dennis M. Hedderich: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Supervision, Funding acquisition. Christian Sorg: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Supervision, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Subsequent data stratification according to newborn birth-weight categories, i.e., small (n = 39), appropriate (n = 50) or large (n = 49) for gestational age , showed an interaction between GDM and birth-weight categories for intermediate-density lipoproteins (IDL)-cholesterol content and IDL- and low-density lipoproteins (LDL)-triglyceride content, and the number of medium very low-density lipoproteins (VLDL) and LDL particles specifically in AGA neonates. Moreover, in a 2-year follow-up study, we observed that small LDL particles were independently associated with offspring obesity at 2 years (n = 103). Collectively, our data demonstrate that GDM disturbs triglyceride and cholesterol lipoprotein content across birth-weight categories, with AGA neonates born to GDM mothers displaying a profile more similar to that of adults with dyslipidemia. Furthermore, an altered fetal lipoprotein pattern was associated with the development of obesity at 2 years.Abnormal lipid metabolism is associated with gestational diabetes mellitus (GDM) and is observed in neonates with abnormal fetal growth. However, the underlying specific changes in the lipoprotein profile remain poorly understood. Thus, in the present study we used a novel nuclear magnetic resonance (NMR)-based approach to profile the umbilical cord serum lipoproteins. Two-dimensional diffusion-ordered 1H-NMR spectroscopy showed that size, lipid content, number and concentration of particles within their subclasses were similar between offspring born to control ( Fetal growth and development constitute a particularly vulnerable period in life that is greatly affected by the maternal environment. Prenatal exposure to nutritional stressors has been associated with fetal programming, which can impact both metabolism and physiology and, consequently, predispose to later development of cardiovascular and metabolic diseases, including obesity . In thisBirth-weight is strongly determined by neonatal fat mass and gestational age, and fetal growth disorders can result from impaired maternal and fetal lipid metabolism. In fact, the levels and composition of cord blood lipids, apolipoproteins and lipoproteins are affected by both maternal and fetal factors ,5,6. DisDiabetic pregnancies are associated with a higher incidence of fetal growth disorders, and there is evidence that disturbances in maternal metabolism strongly contribute to these situations . ChangesInterestingly, 1H-nuclear magnetic resonance (1H-NMR)-based analysis of lipoproteins has established that the number of LDL and HDL particles is a more powerful index of cardiovascular risk than classical cholesterol determinations, given the large variability in the amount of cholesterol per particle and in particle size . 1H-NMR-Given the heterogeneity of growth patterns and the inconclusive findings in the cord blood lipoprotein profile of infants from GDM mothers, a comprehensive characterization of the main lipoproteins, including the assessment of the size and number of particles, is necessary to identify possible alterations in fetal lipoprotein metabolism and their potential consequences for fetal health. Thus, in the present study, we used the Liposcale test, a novel advanced lipoprotein assessment method based on 2D diffusion-ordered 1H-NMR , to examAll the mother–offspring pairs included in this study belonged to a carefully selected pre-birth cohort, and the mothers gave birth at the Department of Obstetrics of the Hospital Universitari de Tarragona Joan XXIII between June 2010 and May 2017. GDM and control women were recruited at their first prenatal visit to the Obstetrics Department and were followed until delivery. In this study, the participants were stratified according to the birth-weight category, and we included a similar proportion of infants born to mothers with GDM and control women who were AGA, SGA or LGA. The inclusion criteria at the end of pregnancy were: (1) singleton pregnancy, (2) accurate gestational age confirmed by an ultrasound examination before 20 weeks of gestation, (3) absence of fetal anomalies, (4) cord blood serum availability and (5) maternal lipid information from the third trimester exam. Neonates with major congenital anomalies, intrauterine infections or born from women with chronic and inflammatory diseases were excluded. One hundred and thirty-six neonate-and-mother pairs fulfilled these criteria (74 control pairs and 62 pairs with GDM) and were included in this study, which was performed in accordance with the tenets of the Declaration of Helsinki and whose protocol was reviewed and approved by the Hospital Universitari de Tarragona Research Ethics Board (ref: 243/2016). All participants provided informed consent before inclusion.All mothers were screened for GDM between 24–28 weeks of pregnancy following the Spanish Diabetes and Pregnancy Group recommendations . SubjectCare for GDM was managed according to the Spanish guidelines for diagnosis and therapy of GDM . Women wMaternal and umbilical cord blood samples were stored in a biobank collection along with the associated clinical data.2 and final weight/height (m)2, respectively. Similarly, gestational weight gain (GWG) was calculated as final weight—pre-pregnancy weight.Demographic and obstetric information on participants was collected via an interviewer-administered questionnaire, which paid particular attention to GDM risk factors. Maternal anthropometry included height, pre-pregnancy weight and weight at the end of pregnancy. Pre-pregnancy weight was self-reported and compared with the weight recorded in the first prenatal visit (before the 10th week of pregnancy) to ensure concordance. Pre-pregnancy body mass index (BMI) and final BMI were calculated as pre-pregnancy weight (kg)/height (m)3. Suprailiac skinfold thickness was measured within the first 48 h of life and was used to calculate the fat mass percentage. [Infant data included sex, gestational age, method of delivery and anthropometry. Neonatal length and weight were measured after delivery using a measuring board to the nearest 0.1 cm and a calibrated scale to the nearest 10 g. Ponderal index (PI) was calculated as birth weight (g)/length (cm)centage.  Neonatescentage. . InfantsHeight and weight information from birth up to 2 years of age was collected for 103 children. We defined obesity as a BMI ≥ 85th percentile according to age- and sex-specific BMI tables of the WHO growth standards .Umbilical cord blood was obtained immediately after delivery. Serum was immediately separated by centrifugation, divided into aliquots and stored at −80 °C until further analysis.Maternal fasting serum samples were obtained between gestational week 33 and 36 to determine glucose, triglycerides, total and HDL-cholesterol in an ADVIA 2400  autoanalyzer by standard enzymatic methods . LDL choCord blood serum samples were analyzed using the 2D diffusion-ordered 1H-NMR-based Liposcale test  . This tet-test or Mann–Whitney U test. One-way analysis of variance (ANOVA) was used to test differences among three or more groups. Potential interactions between GDM and birth-weight categories were assessed by two-way ANOVA followed by Bonferroni post hoc test to adjust for multiple comparisons. Spearman’s rank correlation coefficients were used for the analysis of the relationships between 1H-NMR-assessed lipoprotein profile and maternal and offspring metabolic and clinical variables. To control the false discovery rate (FDR), the Benjamini–Hochberg (B–H) procedure was used, and only those values significant with the B–H correction were considered [p < 0.05 was considered statistically significant.SPSS software v20.0  was used for statistical analysis. Data were presented as percentages for categorical variables, mean (±SD) for normally distributed continuous variables, and median (interquartile range) for non-normally distributed variables. Normal distribution of the data was tested with the Kolmogorov–Smirnov test. Non-normally distributed quantitative variables were used after log10 transformation, when required. For comparisons of proportions, differences between groups were analyzed using the chi-square test, while comparisons between normally and non-normally distributed quantitative variables were performed using unpaired nsidered . Logistip = 0.023) and fasting glucose  were lower in the GDM group of women treated only with diet compared to those who needed insulin therapy (data not shown). In the 1HNMR-assessed lipoprotein profile, no difference was observed between the two groups, except for a lower concentration of medium HDL-P in GDM treated with insulin compared with those treated with diet  (data not shown).Clinical and metabolic characteristics of the two groups, i.e., control and GDM, are shown in n = 62) and control (n = 74) mothers were categorized into three groups based on birth-weight categories according to age- and sex-weight specific charts (p = 0.321) (data not shown).One hundred and thirty-six neonates born to GDM  was highest in the SGA group and lowest in the LGA group, which was consistent with the differences observed among particle sizes . The number of LDL-P was lower in the SGA group than in the AGA and LGA groups, and the same distribution was observed for large and small LDL-P. The number of HDL-P was highest in the AGA group and showed an inverse U distribution when compared with the LGA and SGA groups. This phenomenon was observed specifically for small particles .While the cord blood lipoprotein profile was similar in offspring born to GDM and control mothers, we found some interactions when we assessed the effect of both birth-weight categories and GDM. AGA neonates born to GDM mothers had higher IDL-cholesterol and -triglyceride content, and higher LDL-triglyceride content than the SGA and LGA groups, in contrast to those born to control mothers who had lower concentrations. The same pattern was also observed with medium VLDL-P and LDL-P, which followed an inversed U distribution .Subsequently, we examined the relationship between the 1H-NMR-based lipoprotein profile and the maternal clinical and laboratory parameters as well as the neonatal outcomes. This analysis was conducted separately in both GDM and control groups, and results are shown in LDL-cholesterol concentrations were negatively associated with birth weight in the GDM group, while no association was observed in the control group. Cord blood insulin was strongly and positively associated with small and large LDL-P and LDL-cholesterol content in the control group, while a negative relationship was observed with VLDL-P and VLDL-triglyceride content in the GDM offspring.In both groups, neonatal adiposity was negatively correlated with cord blood VLDL-P, VLDL- and IDL-triglyceride content, and positively associated with cord blood LDL-cholesterol content and LDL-P, and these associations were stronger in the control group.2; p = 0.036), had higher birth weight  and were more exposed to GDM during the intrauterine life compared with normal-weight children . Additionally, they showed a higher number of cord blood small  and large LDL-P . No other differences were observed between the two groups. To further assess the independence of these associations, we performed logistic regression analysis. We found that small LDL-P were associated with infant obesity at 2 years after adjusting for potential confounders (p = 0.023), whereas large LDL-P showed a trend (p = 0.058)  . No diffBoth GDM and abnormal growth patterns have been associated with long-term adverse outcomes in offspring. Similarly, changes in the lipoprotein composition have been proposed as potential markers of cardiovascular diseases later in life. Taking advantage of a thorough lipoprotein profiling based on 1H-NMR, we showed for the first time that GDM modifies the umbilical cord blood lipoprotein profile in AGA neonates. In particular, GDM alters IDL lipoproteins, triglyceride content in LDL, and medium-size VLDL-P and LDL-P in those children. By contrast, GDM offspring belonging to the LGA and SGA groups have lipoprotein profiles more similar to those of controls. Additionally, we found that cord blood small LDL-P, known to be associated with atherosclerosis development, had predictive value for later obesity in the offspring.Both under and overnutrition in utero affects the lipoprotein profile of neonates ,5,6,7. SSince treatment with insulin at the end of pregnancy may activate placental nutrient transport to the fetus and promote placental fatty acid transfer , we asseDespite the differences observed in some lipoproteins and lipid particles in AGA infants of GDM women and controls, these differences were not seen in the rest of the lipoprotein particles or in the groups characterized by more severe alterations in growth, SGA and LGA, suggesting that in these cases, the pattern of growth and fat accumulation could dilute the effect produced by GDM. However, this has to be interpreted cautiously since evidence of cord blood lipoproteins as biomarkers for cardiovascular disease later in life is still scarce. In this regard, results in adult life are often extrapolated to fetal life, despite the fact that they might have a different interpretation. In fact, differences in lipoprotein composition between adults and fetuses have been described, including excess apoE on fetal HDL particles, which are large in size, lack paraoxonase I, and might have lower anti-oxidant capacity ,36, as wPrevious studies exploring the potential relationship between prenatal lipid metabolism and adverse metabolic outcomes in offspring have generated inconsistent results ,41,42,43There is evidence that an altered fetal lipoprotein profile is associated with aorta intima thickness in SGA and LGA neonates ,27, indiOne of the main limitations in observational studies is the inability to attribute causation between the observed associations. However, we considered several critical confounding variables to mitigate bias in the analysis. Thus, the main prenatal factors were addressed, and the groups were comparable for maternal BMI and birth-weight categories. Of note, to reach a sufficient sample size in the three birth-weight categories, the SGA and LGA groups were overrepresented, and further population-based studies are needed to determine the role of lipoprotein composition and subfractions in the pathogenesis of metabolic diseases in offspring. Furthermore, given the relevance of the placenta in the passage of nutrients and in the regulation of fetal metabolism and growth, it would be important to establish whether there is a parallelism between the lipoprotein pattern and placental metabolic pathways.The strengths of this study include a longitudinal birth cohort with almost complete maternal data that establish a temporal relationship between the outcome and the exposure to GDM. The novelty of the lipoprotein assessment, which allowed us to identify different fetal metabolic behaviors, is also a big asset in the experimental methods.GDM disturbs triglyceride and cholesterol lipoprotein concentrations across birth categories, with GDM-AGA neonates showing a profile more similar to that of adults with dyslipidemia and atherosclerosis than those born to normal glucose-tolerant mothers. Moreover, an altered fetal lipoprotein pattern is associated with obesity development at 2 years. Overall, these findings suggest that the fetal lipoprotein profile might be an early biomarker for the development of later diseases.