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ObjectiveThis study aimed to develop and validate a model for predicting extrauterine growth restriction (EUGR) in preterm infants born ≤34 weeks gestation.MethodsPreterm infants from Guangxi Maternal and Child Health Hospital (2019–2021) were randomly divided into training (80%) and testing (20%) sets. Collinear clinical variables were excluded using Pearson correlation coefficients. Predictive factors were identified using Lasso regression. Random forest (RF), support vector machine (SVM), and logistic regression (LR) models were then built and evaluated using the confusion matrix, area under the curve (AUC), and the F1 score. Additionally, calibration curves and decision curve analysis (DCA) were plotted to assess the performance and practical utility of the models.ResultsThe study included 387 infants, with no significant baseline differences between training (n = 310) and testing (n = 77) sets. LR identified gestational age, birth weight, premature rupture of membranes, patent ductus arteriosus, cholestasis, and neonatal sepsis as key EUGR predictors. The RF model (19 variables) demonstrated an accuracy of greater than 90% during training, and superior AUC (0.62), F1 score (0.80), and accuracy (0.72) in testing compared to other models.ConclusionsGestational age, birth weight, premature rupture of membranes, patent ductus arteriosus, cholestasis, and neonatal sepsis are significant EUGR predictors in preterm infants ≤34 weeks. The model shows promise for early EUGR prediction in clinical practice, potentially enhancing screening efficiency and accuracy, thus saving medical resources.

This review aims to explore the current application of Cranial Ultrasound Screening (CUS) in the diagnosis and treatment of brain diseases in extremely preterm infants. It also discusses the potential role of emerging ultrasound-derived technologies such as Super Microvascular Structure Imaging (SMI), Shear Wave Elastography (SWE), Ultrafast Doppler Ultrasound (UfD), and 3D ventricular volume assessment and automated segmentation techniques in clinical practice. A systematic search of medical databases was conducted using the keywords ''(preterm OR extremely preterm OR extremely low birth weight) AND (ultrasound OR ultrasound imaging) AND (neurodevelopment OR brain development OR brain diseases OR brain injury OR neuro*)'' to identify relevant literature. The titles, abstracts, and full texts of the identified articles were carefully reviewed to determine their relevance to the research topic. CUS offers unique advantages in early screening and monitoring of brain diseases in extremely preterm infants, as it can be performed at the bedside without the need for anesthesia or special monitoring. This technique facilitates early detection and intervention of conditions such as intraventricular hemorrhage, white matter injury, hydrocephalus, and hypoxic-ischemic injury in critically ill preterm infants. Continuous refinement of the screening and follow-up processes provides reliable clinical decision-making support for healthcare professionals and parents. Emerging ultrasound technologies, such as SWE, SMI, and UfD, are being explored to provide more accurate and in-depth understanding of brain diseases in extremely preterm infants. SWE has demonstrated its effectiveness in assessing the elasticity of neonatal brain tissue, aiding in the localization and quantification of potential brain injuries. SMI can successfully identify microvascular structures in the brain, offering a new perspective on neurologic diseases. UfD provides a high-sensitivity and quantitative imaging method for the prevention and treatment of neonatal brain diseases by detecting subtle changes in red blood cell movement and accurately assessing the status and progression of brain diseases. CUS and its emerging technologies have significant applications in the diagnosis and treatment of brain diseases in extremely preterm infants. Future research aims to address current technical challenges, optimize and enhance the clinical decision-making capabilities related to brain development, and improve the prevention and treatment outcomes of brain diseases in extremely preterm infants.

Neonatal sleep staging is crucial for understanding infant brain development and assessing neurological health. This study explores the optimal electrode configuration to reduce technical complexities and potential risks of causing skin irritation to neonates during data collection. A Multi-Branch Convolutional Neural Network (CNN) is used to categorize neonatal sleep states based on single-channel Electroencephalography (EEG) data. The proposed model was trained and tested on 16803 30-second segments from 64 infants, all of whom were at post-menstrual age between 36 and 43 weeks at the Children’s hospital of Fudan University. A total of 74 extracted time and frequency domain linear and non-linear features are applied to improve the performance of a Multi-Branch CNN-based classification model. Additionally, using principal component analysis (PCA), feature selection and feature importance are also applied to identify the most important features. Notably, the F3 channel outperforms other single-channels and has accuracy and kappa values 74.27±0.80% and 0.61, respectively. Furthermore, a combination of four left-side electrodes yields slightly better classification accuracy (75.36±0.57%) compared to the four right-side electrodes (74.76±1.10%), with corresponding kappa values of 0.63 and 0.62, respectively. In addition to providing insights into optimal electrode configuration using single-channel and multi-channel EEG data, the results highlight the critical role played by specific EEG channels in sleep stage classification. This research has the potential to enhance neonate care and monitor sleep more effectively, enabling early detection of sleep-related abnormalities such as sleep disorders. Furthermore, this research effectively captures information from a single-channel, reducing computing load while maintaining commendable performance. Additionally, integrating time and frequency domain linear and non-linear features into neonatal sleep staging can enhance accuracy and provide a deeper insight into the complex dynamics and irregularities of newborn’s sleep patterns.

A limited number of electroencephalography (EEG) channels are useful for neonatal sleep classification, particularly in the Internet of Medical Things (IoMT) field, where compact and lightweight devices are essential to monitoring health effectively. A streamlined and cost-effective IoMT solution can be achieved by utilizing fewer EEG channels, thereby reducing data transmission and device processing requirements. Using only two channels of an EEG device, this study presents a binary and multistage classification of neonatal sleep. The binary classification (sleep vs awake) achieved an accuracy of 87.56%, and a Cohen’s kappa of 74.13%. The quiet sleep ( $Q_{S}$ ) detection accuracy was 95.63%, with a Cohen’s kappa of 83.87%. For the three-stage classification, accuracy was 83.72%, and Cohen’s kappa was 69.73%. With only two channels, these are the highest performance parameters. The focus is on the fusion of features extracted through flexible analytical wavelet transform (FAWT) & discrete wavelet transform (DWT), ensemble-based voting models, and fewer channels. To feed crucial features into the ensemble-based voting model, feature importance, feature selection, and validation mechanisms were used. To design the voting classifier, several machine learning models were used, compared, and optimized. With SelectKBest feature selection, the proposed methodology was found to be the most effective. By using only two channels, this study shows the practicality of classifying neonatal sleep stages.

Abstract Background Though there has been an increase in the number of neonates with hypoxic-ischemic encephalopathy (HIE) treated by therapeutic hypothermia (TH) in recent years, the effect of therapeutic hypothermia on mild HIE neonates is still uncertain. Objectives This study aims to explore the safety and efficacy of therapeutic hypothermia in neonates with mild HIE. Methods Retrospectively collected between January 2010 to December 2022 at Children’s Hospital of Fudan University, neonates with mild HIE were divided into TH and non-TH groups. Clinical data of the mild HIE neonates and their mothers’ general information during pregnancy were collected. SPSS 23.0 was used to compare the general condition, the incidence of adverse events, and efficacy in the two groups. Results A total of 71 neonates with mild HIE were included, including 31 in the TH group and 40 in the non-TH group. Compared with the non-TH group, the TH group had significantly lower 5-minute Apgar scores [6 (5–7) points vs. 7 (5–8) points, p = 0.033 ], but a higher rate of tracheal intubation at birth (68%, 21/31 vs. 40%, 16/40, p = 0.02), a higher rate of chest compressions > 30 s (39%, 12/31 vs. 15%, 6/40, p = 0.023), the later initiation enteral feeding [4 (3–4) days vs. 1 (1–2) days, p

Objective: We aimed to investigate the clinical and genetic risk factors associated with neonatal severe unconjugated hyperbilirubinemia.Methods: This was a retrospective, 1:1 matched, case–control study. We included 614 neonates diagnosed with severe unconjugated hyperbilirubinemia (serum total bilirubin level ≥425 μmol/L or serum total bilirubin concentration that met exchange transfusion criteria) from the China Neonatal Genomes Project in Children’s Hospital of Fudan University. Clinical exome sequencing data were analyzed using a data analysis pipeline of Children’s Hospital of Fudan University. The factors associated with severe unconjugated hyperbilirubinemia were assessed using univariable and multivariable logistic regression analyses. Interaction analyses were examined between clinical and genetic risk factors.Results: ABO/Rh incompatibility hemolysis (odds ratio [OR] 3.36, 95% confidence interval [CI] 2.32–4.86), extravascular hemorrhage (OR 2.95, 95% CI 2.24–3.89), weight loss (OR 5.46, 95% CI 2.88–10.36), exclusive breastmilk feeding (OR 3.56, 95% CI 2.71–4.68), and the homozygous mutant of UGT1A1 211G>A (OR 2.35, 95% CI 1.54–3.59) were all identified as factors significantly associated with severe unconjugated hyperbilirubinemia. The presence of UGT1A1 211G>A mildly increased the risk of severe unconjugated hyperbilirubinemia caused by ABO/Rh incompatibility hemolysis (OR 3.98, 95% CI 2.19–7.23), although the effect is not statistically significant.Conclusion: ABO/Rh incompatibility hemolysis, extravascular hemorrhage, weight loss, exclusive breastmilk feeding, and the homozygous mutant of UGT1A1 211G>A were found to be risk factors for severe unconjugated hyperbilirubinemia. Clinical factors remain the most crucial and preventable determinants in managing severe unconjugated hyperbilirubinemia, with a minimal genetic contribution. The establishment of preconception care practices and the reinforcement of screening for the aforementioned risk factors are essential steps for preventing severe unconjugated hyperbilirubinemia.

Abstract Background Carbapenem-resistant gram-negative bacilli (CR-GNB) have been increasingly reported in China. However, dynamic monitoring data on molecular epidemiology of CR-GNB are limited in pediatric patients. Results 300 CR-GNB isolates (200 Carbapenem-resistant K. pneumoniae (CRKP), 50 carbapenem-resistant A.baumannii (CRAB) and 50 carbapenem-resistant P. aeruginosa (CRPA)) were investigated. The predominant carbapenemase gene was bla NDM−1 (73%) and bla KPC−2 (65%) in neonates and non-neonates. Meanwhile, the predominant STs were ST11 (54%) in neonates and ST17 (27.0%) and ST278 (20.0%) in non-neonates. Notably, a shift in the dominant sequence type of CRKP infections from ST17 /ST278-NDM-1 to ST11-KPC-2 was observed during the years 2017–2021 and KPC-KP showed relatively higher resistance to aminoglycosides and quinolones than NDM-KP.Bla OXA−23 was isolated from all the CRAB isolates while only one isolate expressing bla BIC and 2 isolates expressing bla VIM−2 were found in CRPA isolates. ST195 (22.0%) and ST244 (24.0%) were the most common in CRAB and CRPA isolates and all the STs of CRAB belonged to CC92 while CRPA presents ST types with diversity distribution. Conclusion CRKP showed different molecular phenotypes in neonates and non-neonates and was changing dynamically and high-risk clone of ST11 KPC-KP should be paid more attention. Most CRKP and CRAB strains shared the same CCs, suggesting that intrahospital transmission may occur, and large-scale screening and more effective measures are urgently needed.

ObjectiveTo explore the clinical characteristics and prognosis of febrile seizure in children with COVID-19.MethodsThis study is a single-center retrospective cohort study. The cases included febrile seizures in children with COVID-19 admitted to the Renji Hospital from April 7th, 2022 to June 2nd, 2022. We compared children with and without febrile seizures in their clinical characteristics such as sex, age, symptoms, seizure manifestation, COVID-19 severity, and SARS-CoV-2 nucleic acid test results. The children with febrile seizures were followed up by telephone and outpatient service about one month after the nucleic acid turned negative and discharged from the hospital.ResultsA total of 585 cases of children with COVID-19 were included in the analysis. There were 15 children (1.8%) with febrile seizures, age from six months to three years old, nine boys (60.0%) and six girls (40.0%). The manifestations of febrile seizures were all generalized tonic-clonic seizures. The median nucleic acid negative conversion time was 11 (IQR:10.75,13) days. Our first comparison involved comparing children without underlying diseases; there was no significant difference in sex, COVID-19 severity, and clinical manifestations, but there was an age difference (2 vs. 1.3, P = 0.047). There was no difference in SARS-CoV-2 nucleic acid negative time between the two groups (11d vs. 13d, P = 0.128). One child had new clinical manifestations during the follow-up, but his EEG and MRI were normal.ConclusionFebrile seizure may be children's primary neurological manifestation of COVID-19. It may occur in children with no history of epilepsy and is not associated with severe illness. The long-term neurological outcomes of these children should be followed up.

Most existing neonatal sleep staging appro- aches applied multiple EEG channels to obtain good performance. However, it potentially increased the computational complexity and led to an increased risk of skin disruption to neonates during data acquisition. In this paper, a multi-scale hierarchical neural network (MS-HNN) with a squeeze and excitation (SE) block for neonatal sleep staging is presented in this study on the basis of a single EEG channel. MS-HNN composes of multi-scale convolutional neural network (MSCNN), temporal information learning (TIL) module, and squeeze and excitation (SE) block. MSCNN can extract features from different scales and frequencies, and TIL module can acquire the transition information among adjacent stages. In addition, for these extracted features, SE block can selectively concentrate on informative features and weaken redundant features for achieving better performance. The proposed approach was validated on a clinical dataset involving 64 neonates from the Children’s Hospital of Fudan University (CHFU). The proposed network achieves an accuracy of 75.4% and 76.5% for three-class automatic neonatal sleep staging with the single-EEG channel and the eight-EEG channels, respectively. The experimental results show that the proposed method can maintain good performance by making full use of the information in the single channel while reducing the channels to control the computational overhead.

Humans have the ability to appreciate and create music. However, why and how humans have this distinctive ability to perceive music remains unclear. Additionally, the investigation of the innate perceiving skill in humans is compounded by the fact that we have been actively and passively exposed to auditory stimuli or have systematically learnt music after birth. Therefore, to explore the innate musical perceiving ability, infants with preterm birth may be the most suitable population. In this study, the auditory brain networks were explored using dynamic functional connectivity-based reliable component analysis (RCA) in preterm infants during music listening. The brain activation was captured by portable functional near-infrared spectroscopy (fNIRS) to simulate a natural environment for preterm infants. The components with the maximum inter-subject correlation were extracted. The generated spatial filters identified the shared spatial structural features of functional brain connectivity across subjects during listening to the common music, exhibiting a functional synchronization between the right temporal region and the frontal and motor cortex, and synchronization between the bilateral temporal regions. The specific pattern is responsible for the functions involving music comprehension, emotion generation, language processing, memory, and sensory. The fluctuation of the extracted components and the phase variation demonstrates the interactions between the extracted brain networks to encode musical information. These results are critically important for our understanding of the underlying mechanisms of the innate perceiving skills at early ages of human during naturalistic music listening.

Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant’s movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children’s Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures.

Abstract Objective In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke® video recording of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required.

Abstract Background Necrotizing enterocolitis (NEC) is the leading cause of death among preterm infants born at

In recent times, with the advancement of digital imaging, automatic facial recognition has been intensively studied for adults, while less for neonates. Due to the miniature facial structure and facial attributes, newborn facial recognition remains a challenging area. In this paper, an automatic video-based Neonatal Face Attributes Recognition (NFAR) approach in a hierarchical framework is proposed by coalescing the intensity-based method, pose estimation, and novel dedicated neonatal Face Feature Selection (FFS) algorithm. The intensity-based method is used for face detection, followed by the facial pose estimation algorithm and FFS are dedicated to neonatal pose and face feature recognition, respectively. In this study, video-data of 19 neonates' were collected from the Children's Hospital affiliated to Fudan University, Shanghai, to evaluate the proposed NFAR approach. The results show promising performance to detect the neonatal face, pose estimation (-45°, 45°), and facial features (nose, mouth, and eyes) recognition. The NFAR approach exhibits a sensitivity, accuracy, and specificity of 98.7%, 98.5%, and, 95.7% respectively, for the newborn babies at the frontal (0°) facial region. The neonatal face and its attributes recognition can be expected to detect neonate's medical abnormalities unobtrusively by examining the variation in newborn facial texture pattern.

Objective: Classification of sleep-wake states using multichannel electroencephalography (EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) neural network is developed to classify sleep-wake states using multichannel bipolar EEG signals, which takes an input vector of size 108 containing the joint features of 9 channels. The network avoids any post-processing step in order to work as a full-fledged real-time application. For training and testing the model, EEG recordings of 3525 30-second segments from 19 neonates (postmenstrual age of 37 ± 05 weeks) are used. Results: For sleep-wake classification, mean Cohen's kappa between the network estimate and the ground truth annotation by human experts is 0.62. The maximum mean accuracy can reach up to 83% which, to date, is the highest accuracy for sleep-wake classification.

For infants admitted at neonatal intensive care unit, the continuous monitoring of health parameters is critical for their optimal treatment and outcomes. So it's crucial to provide proper treatment, accurate and comfortable monitoring conditions for newborn infants. In this paper, we propose wearable sensor systems integrated with flexible material based non-invasive sensors for neonatal monitoring. The system aims at providing reliable vital signs monitoring as well as comfortable clinical environments for neonatal care. The system consists of a smart vest and a cloud platform. In the smart vest, a novel stretching sensor based on Polydimethylsiloxane-Graphene (PDMS-Graphene) compound is created to detect newborns' respiration signal; textile-based dry electrodes are developed to measure Electrocardiograph (ECG) signals; inertial measurement units (IMUs) are embedded to obtain movement information including accelerated speed and angular velocity of newborn wrists. Experiments were conducted to systematically test the sensing related characteristics of the aforementioned flexible materials and the performance of the proposed multi-sensor platform. The results show that the proposed system can achieve high quality signals. The wearable sensor platform is promising for continuous long term monitoring of neonates. The multi-modal physiological and behavioral signals measured by the platform can be further processed for clinical decision support on the neonatal health status.

Hypoxic-ischemic (HI) injury is one of the initial factors contributing to neonatal brain injury. Src family kinases (SFKs) are considered to act as molecular hubs for N-methyl-d-aspartate receptor (NMDAR) regulation and participate in the HI injury process. The objectives of this study were to evaluate the levels of phospho-Src (p-Src), the relationship between NMDARs and SFKs, and the effects of SFK inhibition on an immature rat HI brain injury model. The model was induced in 3-day-old Sprague–Dawley rats using the Rice-Vannucci model operation. The level of p-Src was evaluated using Western blotting. The association of NMDARs with SFKs was detected using Western blotting and coimmunoprecipitation. After intraperitoneal injection of 4-amino-5-(4-chlorophenyl)-7-(t-butyl) pyrazolo [3,4-d] pyrimidine (PP2), an SFK-selective inhibitor, neuropathological changes were observed by performing H&E and immunofluorescence staining, and the neurological functions were assessed using the following behavioral tests: modified neurological severity score, open field test, and Morris water maze test. The levels of p-Src first decreased at 0 h after injury, increased at 2 h after injury, and continuously decreased from 6 h to 3 days. Along with the increased p-Src levels observed at 2 h after injury, the phosphorylation of NMDAR subunit NR2B at tyrosine 1472 was increased. Following the administration of PP2, the increased p-Src and NMDAR-2B levels detected at 2 h after injury were decreased, and tissue injury and myelin basic protein expression were improved at 7 days after injury. The PP2 intervention improved the performance of injured rats on behavioral tests. In conclusion, we determined the patterns of p-Src expression after HI brain injury in immature rats and showed a relationship with the activated NMDA receptor. The inhibition of p-Src ameliorates neuropathological changes and damages neurological functions induced by HI injury.

Background: The Chinese Neonatal Network (CHNN) is a nationwide neonatal network that aims to improve clinical neonatal care quality and short- and long-term health outcomes of infants. This study aims to assess the quality of the Chinese Neonatal Network database by conducting an internal audit of data extraction.Methods: A data audit was performed by independently replicating the data collection and entry process in all 58 tertiary neonatal intensive care units (NICU) participating in the CHNN. Eighty-eight data elements selected for re-abstraction were classified into three categories (critical, important, less important), and agreement rates for original and re-abstracted data were predefined. Three to five records were randomly selected at each site for re-abstraction, including one short- (0–7 days), two medium- (8–28 days), and two long-stay (more than 28 days) cases. Agreement rates for each data item were calculated for individual NICUs and across the network, respectively.Results: A total of 283 cases and 24,904 data fields were re-abstracted. The agreement rates for original and re-abstracted data elements were 96.1% overall, and 97.2, 94.3, and 96.6% for critical, important, and less important data elements, respectively. Individual site variation for discrepancies ranged between 0.0 and 18.4% for all collected data elements.Conclusion: The completeness, precision, and quality of data in the CHNN database are high, providing assurance for multipurpose use, including health service evaluation, quality improvement, clinical trials, and other research.

Neonatal metabolic acidosis (NMA) is a common problem, particularly in critically ill patients in neonatal intensive care units (NICUs). Complex etiologies and atypical clinical signs make diagnosis difficult; thus, it is crucial to investigate the underlying causes of NMA rapidly and provide disorder-specific therapies. Our study aims to provide an overview of the genetic causes of NMA in patients from NICUs. We performed next-generation sequencing (NGS) on neonates with NMA from January 2016 to December 2019. Clinical features, genetic diagnoses, and their effects on clinical interventions were collected for analysis. In the 354 enrolled patients, 131 (37%) received genetic diagnoses; 95 (72.5%) of them were autosomal recessively inherited diseases. Two hundred and fifteen variants spanning 57 genes were classified as pathogenic (P) or likely pathogenic (LP) in 131 patients. The leading cause was metabolic disorders due to 35 genes found in 89 patients (68%). The other 42 NMA patients (32%) with 22 genes had malformations and renal, neuromuscular, and immune-hematological disorders. Seven genes (MMUT, MMACHC, CHD7, NPHS1, OTC, IVD, and PHOX2B) were noted in more than four patients, accounting for 48.9% (64/131) of the identified P/LP variants. Forty-six diagnosed patients with uncorrected NMA died or gave up. In conclusion, 37% of neonates with metabolic acidosis had genetic disorders. Next-generation sequencing should be considered when investigating the etiology of NMA in NICUs. Based on early molecular diagnoses, valuable treatment options can be provided for some genetic diseases to achieve better outcomes.

Music contains substantial contents that humans can perceive and thus has the capability to evoke positive emotions. Even though neonatal intensive care units (NICUs) can provide preterm infants a developmental environment, they still cannot fully simulate the environment in the womb. The reduced maternal care would increase stress levels in premature infants. Fortunately, music intervention has been proved that it can improve the NICU environment, such as stabilize the heart rate and the respiratory rate, reduce the incidence of apnea, and improve feeding. However, the effects of music therapy on the brain development of preterm infants need to be further investigated. In this paper, we evaluated the influence of short-term music therapy on the brain functions of preterm infants measured by functional near-infrared spectroscopy (fNIRS). We began by investigating how premature babies perceive structural information of music by calculating the correlations between music features and fNIRS signals. Then, the influences of short-term music therapy on brain functions were evaluated by comparing the resting-state functional connectivity before and after the short-term music therapy. The results show that distinct brain regions are responsible for processing corresponding musical features, indicating that preterm infants have the capability to process the complex musical content. However, the results of network analysis show that short-term music intervention is insufficient to cause the changes in cerebral functional connectivity. Therefore, long-term music therapy may be required to achieve the deserved effects on brain functional connectivity.

ObjectivesCentral nervous system (CNS) infection has a high incidence and mortality in neonates, but conventional tests are time-consuming and have a low sensitivity. Some rare genetic diseases may have some similar clinical manifestations as CNS infection. Therefore, we aimed to evaluate the performance of metagenomic next-generation sequencing (mNGS) in diagnosing neonatal CNS infection and to explore the etiology of neonatal suspected CNS infection by combining mNGS with whole exome sequencing (WES).MethodsWe prospectively enrolled neonates with a suspected CNS infection who were admitted to the neonatal intensive care unit(NICU) from September 1, 2019, to May 31, 2020. Cerebrospinal fluid (CSF) samples collected from all patients were tested by using conventional methods and mNGS. For patients with a confirmed CNS infection and patients with an unclear clinical diagnosis, WES was performed on blood samples.ResultsEighty-eight neonatal patients were enrolled, and 101 CSF samples were collected. Fourty-three blood samples were collected for WES. mNGS showed a sample diagnostic yield of 19.8% (20/101) compared to 4.95% (5/101) for the conventional methods. In the empirical treatment group, the detection rate of mNGS was significantly higher than that of conventional methods [27% vs. 6.3%, p=0.002]. Among the 88 patients, 15 patients were etiologically diagnosed by mNGS alone, five patients were etiologically identified by WES alone, and one patient was diagnosed by both mNGS and WES. Twelve of 13 diagnoses based solely on mNGS had a likely clinical effect. Six patients diagnosed by WES also experienced clinical effect.ConclusionsFor patients with a suspected CNS infections, mNGS combined with WES might significantly improve the diagnostic rate of the etiology and effectively guide clinical strategies.

The annular pancreas (AP) is a congenital anomaly of the pancreas that can cause acute abdominal pain and vomiting after birth. However, the genetic cause of AP is still unknown, and no study has reported AP in patients with 17q12 duplication. This study retrospectively analyzed the next-generation sequencing (NGS) data of individuals from January 2016 to June 2020 for 17q12 duplication. To identify the function of the key gene of HNF1B in the 17q12 duplication region, human HNF1B mRNA was microinjected into LiPan zebrafish transgenic embryos. A total of 19 cases of 17q12 duplication were confirmed. AP was diagnosed during exploratory laparotomy in four patients (21.1%). The other common features of 17q12 duplication included intellectual disability (50%), gross motor delay (50%), and seizures/epilepsy (31.58%). The ratio of the abnormal pancreas in zebrafish was significantly higher in the HNF1B overexpression models. In conclusion, we first reported AP in patients with duplication of the 17q12 region, resulting in the phenotype of 17q12 duplication syndrome. Furthermore, our zebrafish studies verified the role of the HNF1B gene in pancreatic development.

Introduction An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred in Wuhan, China starting in December 2019. Yet the clinical features and long-term outcomes of neonates with SARS-CoV-2 exposure are lacking. The purpose of this study is to describe the clinical course and prognosis of the neonates exposed to SARS-CoV-2.Methods and analysis This is a multicentre observational study conducted at the designated children and maternal and child hospitals in the mainland of China. Neonates exposed to SARS-CoV-2 infection will be recruited. The data to be collected via case report forms include demographic details, clinical features, laboratory and imaging results, as well as outcomes. Primary outcomes are the mortality of neonates with COVID-19 and SARS-CoV-2 infection of neonates born to mothers with COVID-19. Secondary outcomes are the birth weight, premature delivery and neurological development of neonates exposed to SARS-CoV-2. The neurological development is assessed by the Chinese standardised Denver Developmental Screening Test at the corrected age of 6 months.Ethics and dissemination This study has been approved by the Children's Hospital of Fudan University ethics committee (No. (2020)31). The study findings will be disseminated in peer-reviewed journals and presented at national and international conferences in order to improve the understanding of the clinical course among neonates exposed to SARS-CoV-2 and to provide evidence-based treatment and prevention strategies for this group.Trial registration number NCT04279899.

Background Congenital heart defects (CHDs) are the most common type of birth defects. The genetic aetiology of CHD is complex and incompletely understood. The overall distribution of genetic causes in patients with CHD from neonatal intensive care units (NICUs) needs to be studied. Methods CHD cases were extracted from the China Neonatal Genomes Project (2016-2021). Next-generation sequencing results and medical records were retrospectively evaluated to note the frequency of genetic diagnosis and the respective patient outcomes. Results In total, 1795 patients were included. The human phenotype ontology term of atrial septal defect, patent ductus arteriosus and ventricular septal defect account for a large portion of the CHD subtype. Co-occurring extracardiac anomalies were observed in 35.1% of patients. 269 of the cases received genetic diagnoses that could explain the phenotype of CHDs, including 172 copy number variations and 97 pathogenic variants. The detection rate of trio-whole-exome sequencing was higher than clinical exome sequencing (21.8% vs 14.5%, p<0.05). Further follow-up analysis showed the genetic diagnostic rate was higher in the deceased group than in the surviving group (29.0% vs 11.9%, p<0.05). Conclusion This is the largest cohort study to explore the genetic spectrum of patients with CHD in the NICU in China. Our findings may benefit future work on improving genetic screening and counselling for NICU patients with CHD. [ABSTRACT FROM AUTHOR]