Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

BACKGROUND: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating. Knowing which tools are most effective remains unclear. To evaluate the performance of computational methods, and to encourage innovation in method development, we designed a Critical Assessment of Genome Interpretation (CAGI) community challenge to place variant prioritization models head-to-head in a real-life clinical diagnostic setting. METHODS: We utilized genome sequencing (GS) data from families sequenced in the Rare Genomes Project (RGP), a direct-to-participant research study on the utility of GS for rare disease diagnosis and gene discovery. Challenge predictors were provided with a dataset of variant calls and phenotype terms from 175 RGP individuals (65 families), including 35 solved training set families with causal variants specified, and 30 unlabeled test set families (14 solved, 16 unsolved). We tasked teams to identify causal variants in as many families as possible. Predictors submitted variant predictions with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on the rank position of causal variants, and the maximum F-measure, based on precision and recall of causal variants across all EPCR values. RESULTS: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performers recalled causal variants in up to 13 of 14 solved families within the top 5 ranked variants. Newly discovered diagnostic variants were returned to two previously unsolved families following confirmatory RNA sequencing, and two novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant in an unsolved proband with phenotypes consistent with asparagine synthetase deficiency. CONCLUSIONS: Model methodology and performance was highly variable. Models weighing call quality, allele frequency, predicted deleteriousness, segregation, and phenotype were effective in identifying causal variants, and models open to phenotype expansion and non-coding variants were able to capture more difficult diagnoses and discover new diagnoses. Overall, computational models can significantly aid variant prioritization. For use in diagnostics, detailed review and conservative assessment of prioritized variants against established criteria is needed.

A resonant high-pressure microsensor based on a composite pressure-sensitive mechanism of diaphragm bending and volume compression.

In this paper, a composite pressure-sensitive mechanism combining diaphragm bending and volume compression was developed for resonant pressure microsensors to achieve high-pressure measurements with excellent accuracy. The composite mechanism was explained, and the sensor structure was designed based on theoretical analysis and finite element simulation. An all-silicon resonant high-pressure microsensor with multiple miniaturized cavities and dual resonators was developed, where dual resonators positioned in two resonant cavities with suitably different widths are used to perform opposite characteristics in pressure and the same characteristics at different temperatures, which can improve pressure sensitivities and realize temperature self-compensation by differential frequency output. The microsensor was fabricated by microfabrication, and the experimental results showed that the sensor had an accuracy of ±0.015% full scale (FS) in a pressure range of 0.1~100 MPa and a temperature range of -10~50 °C. The pressure sensitivity of the differential frequency was 261.10 Hz/MPa (~2523 ppm/MPa) at a temperature of 20 °C, and the temperature sensitivities of the dual resonators were -1.54 Hz/°C (~-14.5 ppm/°C) and -1.57 Hz/°C (~-15.6 ppm/°C) at a pressure of 2 MPa. The differential output had an outstanding stability within ±0.02 Hz under constant temperature and pressure. Thus, this research provides a convenient solution for high-pressure measurements because of its advantages, namely, large range, excellent accuracy and stability.

A MEMS Electrochemical Angular Accelerometer with Silicon-Based Four-Electrode Structure.

This paper presents a MEMS electrochemical angular accelerometer with a silicon-based four-electrode structure, which was made of thousands of interconnected microchannels for electrolyte flow, anodes uniformly coated on structure surfaces and cathodes located on the sidewalls of flow holes. From the perspective of device fabrication, in this study, the previously reported multi-piece assembly was simplified into single-piece integrative manufacturing, effectively addressing the problems of complex assembly and manual alignment. From the perspective of the sensitive structure, in this study, the silicon-based four-electrode structure featuring with complete insulation layers between anodes and cathodes can enable fast electrochemical reactions with improved sensitivities. Numerical simulations were conducted to optimize the geometrical parameters of the silicon-based four-electrode structure, where increases in fluid resistance and cathode area were found to expand working bandwidths and improve device sensitivity, respectively. Then, the silicon-based four-electrode structure was fabricated by conventional MEMS processes, mainly composed of wafer-level bonding and wafer-level etching. As to device characterization, the MEMS electrochemical angular accelerometer with the silicon-based four-electrode structure exhibited a maximum sensitivity of 1458 V/(rad/s2) at 0.01 Hz and a minimum noise level of -164 dB at 1 Hz. Compared with previously reported electrochemical angular accelerometers, the angular accelerometer developed in this study offered higher sensitivities and lower noise levels, indicating strong potential for applications in the field of rotational seismology.

Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases.

BACKGROUND/AIMS: This study evaluates the performance of the Airdoc retinal artificial intelligence system (ARAS) for detecting multiple fundus diseases in real-world scenarios in primary healthcare settings and investigates the fundus disease spectrum based on ARAS. METHODS: This real-world, multicentre, cross-sectional study was conducted in Shanghai and Xinjiang, China. Six primary healthcare settings were included in this study. Colour fundus photographs were taken and graded by ARAS and retinal specialists. The performance of ARAS is described by its accuracy, sensitivity, specificity and positive and negative predictive values. The spectrum of fundus diseases in primary healthcare settings has also been investigated. RESULTS: A total of 4795 participants were included. The median age was 57.0 (IQR 39.0-66.0) years, and 3175 (66.2%) participants were female. The accuracy, specificity and negative predictive value of ARAS for detecting normal fundus and 14 retinal abnormalities were high, whereas the sensitivity and positive predictive value varied in detecting different abnormalities. The proportion of retinal drusen, pathological myopia and glaucomatous optic neuropathy was significantly higher in Shanghai than in Xinjiang. Moreover, the percentages of referable diabetic retinopathy, retinal vein occlusion and macular oedema in middle-aged and elderly people in Xinjiang were significantly higher than in Shanghai. CONCLUSION: This study demonstrated the dependability of ARAS for detecting multiple retinal diseases in primary healthcare settings. Implementing the AI-assisted fundus disease screening system in primary healthcare settings might be beneficial in reducing regional disparities in medical resources. However, the ARAS algorithm must be improved to achieve better performance. TRIAL REGISTRATION NUMBER: NCT04592068.

PICOTEES: a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants from Chinese children cohorts.

The growth in biomedical data resources has raised potential privacy concerns and risks of genetic information leakage. For instance, exome sequencing aids clinical decisions by comparing data through web services, but it requires significant trust between users and providers. To alleviate privacy concerns, the most commonly used strategy is to anonymize sensitive data. Unfortunately, studies have shown that anonymization is insufficient to protect against reidentification attacks. Recently, privacy-preserving technologies have been applied to preserve application utility while protecting the privacy of biomedical data. We present the PICOTEES framework, a privacy-preserving online service of phenotype exploration for genetic-diagnostic variants (https://birthdefectlab.cn:3000/). PICOTEES enables privacy-preserving queries of the phenotype spectrum for a single variant by utilizing trusted execution environment technology, which can protect the privacy of the user's query information, backend models, and data, as well as the final results. We demonstrate the utility and performance of PICOTEES by exploring a bioinformatics dataset. The dataset is from a cohort containing 20,909 genetic testing patients with 3,152,508 variants from the Children's Hospital of Fudan University in China, dominated by the Chinese Han population (>99.9%). Our query results yield a large number of unreported diagnostic variants and previously reported pathogenicity.

Neurodevelopmental Outcomes Prediction in Newborns with Seizures Caused by KCNQ2 Gene Defects.

INTRODUCTION: Pathogenic variant in the KCNQ2 gene is a common genetic etiology of neonatal convulsion. However, it remains a question in KCNQ2-related disorders that who will develop into atypical developmental outcomes. METHODS: We established a prediction model for the neurodevelopmental outcomes of newborns with seizures caused by KCNQ2 gene defects based on the Gradient Boosting Machine (GBM) model with a training set obtained from the Human Gene Mutation Database (HGMD, public training dataset). The features used in the prediction model were, respectively, based on clinical features only and optimized features. The validation set was obtained from the China Neonatal Genomes Project (CNGP, internal validation dataset). RESULTS: With the HGMD training set, the prediction results showed that the area under the receiver-operating characteristic curve (AUC) for predicting atypical developmental outcomes was 0.723 when using clinical features only and was improved to 0.986 when using optimized features, respectively. In feature importance ranking, both variants pathogenicity and protein functional/structural features played an important role in the prediction model. For the CNGP validation set, the AUC was 0.596 when using clinical features only and was improved to 0.736 when using optimized features. CONCLUSION: In our study, functional/structural features and variant pathogenicity have higher feature importance compared with clinical information. This prediction model for the neurodevelopmental outcomes of newborns with seizures caused by KCNQ2 gene defects is a promising alternative that could prove to be valuable in clinical practice.

Comprehensive assessment of the genetic characteristics of small for gestational age newborns in NICU: from diagnosis of genetic disorders to prediction of prognosis.

BACKGROUND: In China, ~1,072,100 small for gestational age (SGA) births occur annually. These SGA newborns are a high-risk population of developmental delay. Our study aimed to evaluate the genetic profile of SGA newborns in the newborn intensive care unit (NICU) and establish a prognosis prediction model by combining clinical and genetic factors. METHODS: A cohort of 723 SGA and 1317 appropriate for gestational age (AGA) newborns were recruited between June 2018 and June 2020. Clinical exome sequencing was performed for each newborn. The gene-based rare-variant collapsing analyses and the gene burden test were applied to identify the risk genes for SGA and SGA with poor prognosis. The Gradient Boosting Machine framework was used to generate two models to predict the prognosis of SGA. The performance of two models were validated with an independent cohort of 115 SGA newborns without genetic diagnosis from July 2020 to April 2022. All newborns in this study were recruited through the China Neonatal Genomes Project (CNGP) and were hospitalized in NICU, Children's Hospital of Fudan University, Shanghai, China. RESULTS: Among the 723 SGA newborns, 88(12.2%) received genetic diagnosis, including 42(47.7%) with monogenic diseases and 46(52.3%) with chromosomal abnormalities. SGA with genetic diagnosis showed higher rates in severe SGA(54.5% vs. 41.9%, P=0.0025) than SGA without genetic diagnosis. SGA with chromosomal abnormalities showed higher incidences of physical and neurodevelopmental delay compared to those with monogenic diseases (45.7% vs. 19.0%, P=0.012). We filtered out 3 genes (ITGB4, TXNRD2, RRM2B) as potential causative genes for SGA and 1 gene (ADIPOQ) as potential causative gene for SGA with poor prognosis. The model integrating clinical and genetic factors demonstrated a higher area under the receiver operating characteristic curve (AUC) over the model based solely on clinical factors in both the SGA-model generation dataset (AUC=0.9[95% confidence interval 0.84-0.96] vs. AUC=0.74 [0.64-0.84]; P=0.00196) and the independent SGA-validation dataset (AUC=0.76 [0.6-0.93] vs. AUC=0.53[0.29-0.76]; P=0.0117). CONCLUSION: SGA newborns in NICU presented with roughly equal proportions of monogenic and chromosomal abnormalities. Chromosomal disorders were associated with poorer prognosis. The rare-variant collapsing analyses studies have the ability to identify potential causative factors associated with growth and development. The SGA prognosis prediction model integrating genetic and clinical factors outperformed that relying solely on clinical factors. The application of genetic sequencing in hospitalized SGA newborns may improve early genetic diagnosis and prognosis prediction.

Primary carnitine deficiency: Estimation of prevalence in Chinese population and insights into newborn screening.

Primary carnitine deficiency (PCD) caused by pathogenic variants in the solute carrier family 22 member 5 (SLC22A5) gene is a rare autosomal recessive disease that results in defective fatty acid oxidation. PCD can be detected through tandem mass spectrometry (MS/MS), but transplacental transport of free carnitine from mothers may cause false negatives or positives during newborn screening (NBS). This study aimed to analyze the genetic characteristics of SLC22A5 and estimate the prevalence of PCD in the Chinese population, providing useful information for NBS and genetic counseling. We manually curated SLC22A5 pathogenic or likely pathogenic (P/LP) variants according to the American College of Medical Genetics and Genomics (ACMG) guidelines and identified 128 P/LP variants. Based on the China Neonatal Genomes Project (CNGP), the estimated PCD prevalence was 1:17,456, which was higher than that in other populations. The genotype-phenotype association analysis showed that patients carrying homozygous c.760C>T and c.844C>T were more likely to present cardiomyopathy, whereas those carrying homozygous c.1400C>G were more likely to be asymptomatic (all p-values < 0.05). We found that there was no significant difference in initial C0 concentrations between patients and carriers, but there was a significant difference in the second-tier screening of C0 concentration between them (p-value < 0.05). We established a cost-effective variant panel containing 10 high-frequency sites and developed a screening algorithm incorporating gene panels with MS/MS, which could rescue one more patient who was undetected from MS/MS. In conclusion, the prevalence of PCD in the Chinese population is relatively high. The combination of conventional NBS with genetic sequencing is suggested for early diagnosis of PCD.

Genetic Spectrum of Congenital Anomalies of the Kidney and Urinary Tract in Chinese Newborn Genome Project.

Introduction: Congenital anomalies of the kidney and urinary tract (CAKUT) corresponds to a spectrum of defects. Several large-cohort studies have used high-throughput sequencing to investigate the genetic risk of CAKUT during antenatal, childhood, and adulthood period. However, our knowledge of newborns with CAKUT is limited. Methods: This multicenter retrospective cohort study explored the genetic spectrum of CAKUT in a Chinese neonatal cohort. Clinical data and whole exome sequencing (WES) data of 330 newborns clinically diagnosed with CAKUT were collected. WES data were analyzed for putative deleterious single nucleotide variants (SNVs) and potential disease-associated copy number variants (CNVs). Results: In this study, pathogenic variants were identified in 61 newborns (18.5%, 61/330), including 35 patients (57.4%) with SNVs, 25 patients (41%) with CNVs, and 1 patient with both an SNV and a CNV. Genetic diagnosis rates were significantly higher in patients with extrarenal manifestations (P＜0.001), especially in those with cardiovascular malformations (P＜0.05). SNVs in genes related to syndromic disorders (CAKUT with extrarenal manifestations) were common, affecting 20 patients (57.1%, 20/35). KMT2D was the most common gene (5 patients) and 17q12 deletion was the most common CNV (4 patients). Patient 110 was detected with both a CNV (17q12 deletion) and an SNV (a homozygous variant of SLC25A13). Among the newborns with positive genetic results, 22 (36.1%, 22/61) patients may benefit from a molecular diagnosis and change in clinical management (including early multidisciplinary treatment, disease-specific follow-up, and familial genetic counseling). Conclusion: This study shows the heterogeneous genetic etiologies in a Chinese CAKUT neonatal cohort by using WES. Patients with CAKUT who have extrarenal manifestations are more likely to harbor genetic diagnoses. Kabuki syndrome and 17q12 deletion syndrome were the most common genetic findings. Approximately 36.1% of the patients may benefit from molecular diagnoses and a change in clinical management.

Genetic spectrums and clinical profiles of critically ill neonates with congenital auricular deformity in the China Neonatal Genomes Project.

Congenital auricular deformity (CAD) is a complex phenotype that may occur as a single malformation or part of a congenital syndrome. The genetic architecture and utility of next-generation sequencing (NGS) in a sizable cross-sectional study of critically ill neonates with CAD have not yet been systematically investigated. This cross-sectional study investigated the genetic spectrum in critically ill neonates with CADs. Critically ill neonates with CADs (n = 251) were enrolled between August 8, 2016 and October 1, 2022. All neonates underwent NGS. The outcomes were molecular diagnostic yield, spectrum of genetic events, and clinical findings. Genetic findings were obtained in 107 neonates (42.6%), of which 67.3% (72/107) had pathogenic/likely pathogenic/variants of uncertain significance (P/LP/VUS) gene variations and 32.7% (35/107) had P/LP/VUS copy number variations (CNVs). The diagnostic rates of clinical exome sequencing were similar to those of exome sequencing. The logistic regression model revealed that CAD neonates with craniofacial abnormalities (OR = 4.15, 95% CI 2.29-7.53) or cardiovascular malformation (OR = 2.09, 95% CI 1.14-3.84) are more likely to be attributed to genetic causes. Follow-up analysis revealed that, compared to those in the undiagnosed group, the number of neonates whose care was withdrawn or who died was higher in the genetically diagnosed group (P < 0.05). This study identified a high incidence of genetic causes in critically ill neonates with CADs, with a combination of single-nucleotide variations and CNVs among the genetic causes of CAD. These findings highlight potential of NGS in the genetic testing of critically ill neonates with CADs.

A novel evolutionary method for parameter-free MEMS structural design and its application in piezoresistive pressure sensors.

In this paper, a novel simulation-based evolutionary method is presented for designing parameter-free MEMS structures with maximum degrees of freedom. This novel design method enabled semiautomatic structure evolution by weighing the attributes of each segment of the structure and yielded an optimal design after multiple iterations. The proposed method was utilized to optimize the pressure-sensitive diaphragm of a piezoresistive pressure sensor (PPS). Finite element method (FEM) simulations revealed that, in comparison to conventional diaphragms without islands and with square islands, the optimized diaphragm increased the stress by 10% and 16% and reduced the nonlinearity by 57% and 77%, respectively. These improvements demonstrate the value of this method. Characterization of the fabricated PPS revealed a high sensitivity of 8.8 mV V-1 MPa-1 and a low nonlinearity of 0.058% FS at 20 °C, indicating excellent sensor performance.

The TRIM37 variants in Mulibrey nanism patients paralyze follicular helper T cell differentiation.

The Mulibrey (Muscle-liver-brain-eye) nanism caused by loss-of-function variants in TRIM37 gene is an autosomal recessive disorder characterized by severe growth failure and constrictive pericarditis. These patients also suffer from severe respiratory infections, co-incident with an increased mortality rate. Here, we revealed that TRIM37 variants were associated with recurrent infection. Trim37 FINmajor (a representative variant of Mulibrey nanism patients) and Trim37 knockout mice were susceptible to influenza virus infection. These mice showed defects in follicular helper T (TFH) cell development and antibody production. The effects of Trim37 on TFH cell differentiation relied on its E3 ligase activity catalyzing the K27/29-linked polyubiquitination of Bcl6 and its MATH domain-mediated interactions with Bcl6, thereby protecting Bcl6 from proteasome-mediated degradation. Collectively, these findings highlight the importance of the Trim37-Bcl6 axis in controlling the development of TFH cells and the production of high-affinity antibodies, and further unveil the immunologic mechanism underlying recurrent respiratory infection in Mulibrey nanism.

Critical assessment of variant prioritization methods for rare disease diagnosis within the Rare Genomes Project.

Background: A major obstacle faced by rare disease families is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years, and causal variants are identified in under 50%. The Rare Genomes Project (RGP) is a direct-to-participant research study on the utility of genome sequencing (GS) for diagnosis and gene discovery. Families are consented for sharing of sequence and phenotype data with researchers, allowing development of a Critical Assessment of Genome Interpretation (CAGI) community challenge, placing variant prioritization models head-to-head in a real-life clinical diagnostic setting. Methods: Predictors were provided a dataset of phenotype terms and variant calls from GS of 175 RGP individuals (65 families), including 35 solved training set families, with causal variants specified, and 30 test set families (14 solved, 16 unsolved). The challenge tasked teams with identifying the causal variants in as many test set families as possible. Ranked variant predictions were submitted with estimated probability of causal relationship (EPCR) values. Model performance was determined by two metrics, a weighted score based on rank position of true positive causal variants and maximum F-measure, based on precision and recall of causal variants across EPCR thresholds. Results: Sixteen teams submitted predictions from 52 models, some with manual review incorporated. Top performing teams recalled the causal variants in up to 13 of 14 solved families by prioritizing high quality variant calls that were rare, predicted deleterious, segregating correctly, and consistent with reported phenotype. In unsolved families, newly discovered diagnostic variants were returned to two families following confirmatory RNA sequencing, and two prioritized novel disease gene candidates were entered into Matchmaker Exchange. In one example, RNA sequencing demonstrated aberrant splicing due to a deep intronic indel in ASNS, identified in trans with a frameshift variant, in an unsolved proband with phenotype overlap with asparagine synthetase deficiency. Conclusions: By objective assessment of variant predictions, we provide insights into current state-of-the-art algorithms and platforms for genome sequencing analysis for rare disease diagnosis and explore areas for future optimization. Identification of diagnostic variants in unsolved families promotes synergy between researchers with clinical and computational expertise as a means of advancing the field of clinical genome interpretation.

Early onset critically ill infants with Schaaf-Yang syndrome: a retrospective study from the China neonatal genomes project and literature review.

Background: Schaaf-Yang syndrome (SYS) is a recently identified rare neurodevelopmental disorder characterized by neonatal hypotonia, feeding difficulty, joint contractures, autism spectrum disorder and development delay/intellectual disability. It is mainly caused by truncating variants in maternally imprinted gene MAGEL2 within the Prader-Willi syndrome critical region 15q11-q13. Clinical diagnosis of SYS is difficult for clinicians due to its rarity and highly variable phenotypes, while unique inheritance patterns also complicate genetic diagnosis. To date, no published papers have analyzed the clinical consequences and molecular changes in Chinese patients. Methods: In this study, we retrospectively investigated the mutation spectrums and phenotypic features of 12 SYS infants. The data were from a cohort of critically ill infants from the China neonatal genomes project (CNGP), sponsored by Children's Hospital of Fudan University. We also reviewed relevant literature. Results: Six previously reported mutations and six novel pathogenic variations of MAGEL2 were identified in 12 unrelated infants. Neonatal respiratory problems were the major complaint for hospitalization, which occurred in 91.7% (11/12) cases. All babies displayed feeding difficulties and a poor suck postnatally, and neonatal dystonia was present in 11 of the cases; joint contractures and multiple congenital defects were also observed. Interestingly, we found that 42.5% (57/134) of the reported SYS patients, including ours carried variants in the c.1996 site, particularly the c.1996dupC variant. The mortality rate was 17.2% (23/134), with the median age of death between 24 gestational weeks in fetuses and 1-month-old in infants. Respiratory failure was the leading cause of death in live-born patients (58.8%, 10/17), especially during the neonatal period. Conclusions: Our findings expanded the genotype and phenotype spectrum of neonatal SYS patients. The results demonstrated that respiratory dysfunction was a typical characteristic among Chinese SYS neonates that should attract physicians' attention. The early identification of such disorders allows early intervention and can further provide genetic counseling as well as reproductive options for the affected families.

Risk stratification of hemodynamically significant patent ductus arteriosus by clinical and genetic factors.

BACKGROUND: Hemodynamically significant patent ductus arteriosus (hsPDA) is associated with increased comorbidities in neonates. Early evaluation of hsPDA risk is critical to implement individualized intervention. The aim of the study was to provide a powerful reference for the early identification of high-risk hsPDA population and early treatment decisions. METHODS: We enrolled infants who were diagnosed with PDA and performed exome sequencing. The collapsing analyses were used to find the risk gene set (RGS) of hsPDA for model construction. The credibility of RGS was proven by RNA sequencing. Multivariate logistic regression was performed to establish models combining clinical and genetic features. The models were evaluated by area under the receiver operating curve (AUC) and decision curve analysis (DCA). RESULTS: In this retrospective cohort study of 2199 PDA patients, 549 (25.0%) infants were diagnosed with hsPDA. The model [all clinical characteristics selected by least absolute shrinkage and selection operator regression (all CCs)] based on six clinical variables was acquired within three days of life, including gestational age (GA), respiratory distress syndrome (RDS), the lowest platelet count, invasive mechanical ventilation, and positive inotropic and vasoactive drugs. It has an AUC of 0.790 [95% confidence interval (CI) = 0.749-0.832], while the simplified model (basic clinical characteristic model) including GA and RDS has an AUC of 0.753 (95% CI = 0.706-0.799). There was a certain consistency between RGS and differentially expressed genes of the ductus arteriosus in mice. The AUC of the models was improved by RGS, and the improvement was significant (all CCs vs. all CCs + RGS: 0.790 vs. 0.817, P < 0.001). DCA demonstrated that all models were clinically useful. CONCLUSIONS: Models based on clinical factors were developed to accurately stratify the risk of hsPDA in the first three days of life. Genetic features might further improve the model performance. Video Abstract (MP4 86834 kb).

Co-infections of Klebsiella pneumoniae and Elizabethkingia miricola in black-spotted frogs (Pelophylax nigromaculatus).

Pelophylax nigromaculatus is a common commercial specie of frogs that generally cultured throughout China. With the application of high-density culture, P. nigromaculatus can be co-infected by two or more pathogens, which thereby induce synergistic influence on the virulence of the infection. In this study, two bacterial strains were simultaneously isolated from diseased frogs by incubating on Luria-Bertani (LB) agar. Isolates were identified as Klebsiella pneumoniae and Elizabethkingia miricola by morphological, physiological and biochemical features, as well as 16S rRNA sequencing and phylogenetic analysis. The whole genome of K. pneumoniae and E. miricola isolates consist single circular chromosome of 5,419,557 bp and 4,215,349 bp, respectively. The genomic sequence analysis further indicated that K. pneumoniae isolate conserved 172 virulent and 349 antibiotic-resistance genes, whereas E. miricola contained 24 virulent and 168 antibiotic resistance genes. In LB broth, both isolates could grow well at 0%-1% NaCl concentration and pH 5-7. Antibiotic susceptibility testing revealed that both K. pneumoniae and E. miricola were resistant to kanamycin, neomycin, ampicillin, piperacillin, carbenicillin, enrofloxacin, norfloxacin and sulfisoxazole. Histopathological studies showed that co-infection caused considerable lesions in the tissues of brain, eye, muscle, spleen, kidney and liver, including cell degeneration, necrosis, hemorrhage and inflammatory cell infiltration. The LD50 of K. pneumoniae and E. miricola isolates were 6.31 × 105 CFU/g and 3.98 × 105 CFU/g frog weight, respectively. Moreover, experimentally infected frogs exhibited quick and higher mortality under coinfection with K. pneumoniae and E. miricola than those single challenge of each bacterium. To date, no natural co-infection by these two bacteria has been reported from frogs and even amphibians. The results will not only shed light on the feature and pathogenesis of K. pneumoniae and E. miricola, but also highlight that co-infection of these two pathogen is a potential threat to black-spotted frog farming.

High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population.

Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.

Comparison of Genetic Profiles of Neonates in Intensive Care Units Conceived With or Without Assisted Reproductive Technology.

Importance: A growing number of children are conceived with assisted reproductive technology (ART). However, there is a lack of studies systematically analyzing the genetic landscape of live-born children conceived through ART who need intensive care in the neonatal period. Objective: To investigate the incidence and type of molecular defects among neonates conceived through ART who are in intensive care units (NICUs) with suspected genetic conditions. Design, Setting, and Participants: This was a cross-sectional study using data from the China Neonatal Genomes Project, a multicenter national neonatal genome data set managed by the Children's Hospital of Fudan University. All participants were from level III and IV NICUs and included 535 neonates conceived through ART with suspected genetic conditions, with data collected between August 1, 2016, and December 31, 2021, and 1316 naturally conceived neonates with suspected genetic conditions in the same clinical settings, with data collected between August 1, 2016, and December 31, 2018. The data were analyzed between September 2021 and January 2023. Exposures: Whole-exome sequencing or target clinical exome sequencing with pathogenic or likely pathogenic single-nucleotide variant (SNV) and copy number variation (CNV) detection was performed for each individual. Main Outcomes and Measures: The primary outcome was the molecular diagnostic yield, mode of inheritance, spectrum of genetic events, and incidence of de novo variants. Results: A total of 535 neonates conceived through ART (319 boys [59.6%]) and 1316 naturally conceived neonates (772 boys [58.7%]) were included. A genetic diagnosis was established for 54 patients conceived through ART (10.1%), including 34 patients with SNVs (63.0%) and 20 with CNVs (37.0%). In the non-ART group, 174 patients (13.2%) received a genetic diagnosis, including 120 patients with SNVs (69.0%) and 54 with CNVs (31.0%). The overall diagnostic yield was comparable between the ART group and the naturally conceived neonates (10.1% vs 13.2%; odds ratio [OR], 0.74; 95% CI, 0.53-1.02), as was the proportion of SNVs (63.0% vs 69.0%; OR, 0.68; 95% CI, 0.46-1.00) and CNVs (37.0% vs 31.0%; OR, 0.91; 95% CI, 0.54-1.53) detected by sequencing. Furthermore, the proportions of de novo variants in the ART group and the non-ART group were similar (75.9% [41 of 54] vs 64.4% [112 of 174]; OR, 0.89; 95% CI, 0.62-1.30). Conclusions and Relevance: This cross-sectional study of neonates in NICUs suggests that the overall genetic diagnostic yield and the incidence of de novo variants were similar between live-born neonates conceived through ART and naturally conceived neonates in the same settings.

An Integrated Pipeline for Trio-Rapid Genome Sequencing in Critically Ill Infants.

Trio-rapid genome sequencing (trio-rGS) can assist the genetic diagnosis of critically ill infants given its ability to detect a broad range of pathogenic variants, as well as microbes, simultaneously with high efficiency. To achieve more comprehensive clinical diagnoses, it is essential to propose a recommended protocol in clinical practice. Here, we introduced an integrated pipeline to detect germline variants and microorganisms simultaneously from trio-RGS in critically ill infants, which provides step-by-step criteria for the semi-automatic processing procedures. With this pipeline in clinical application, only 1 ml of peripheral blood is needed for clinicians to provide both genetic and infectious causal information to a patient. The establishment and clinical practice of the method is of great significance for further mining of high-throughput sequencing data and for assisting clinicians in promoting diagnosis efficiency and accuracy. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Experimental pipeline for rapid whole-genome sequencing for the simultaneous detection of germline variants and microorganisms Basic Protocol 2: Computational pipeline for rapid whole-genome sequencing for the simultaneous detection of germline variants and microorganisms.

An All-Silicon Resonant Pressure Microsensor Based on Eutectic Bonding.

In this paper, an all-Si resonant pressure microsensor based on eutectic bonding was developed, which can eliminate thermal expansion coefficient mismatches and residual thermal stresses during the bonding process. More specifically, the resonant pressure microsensor included an SOI wafer with a pressure-sensitive film embedded with resonators, which was eutectically bonded with a silicon cap for vacuum encapsulation. The all-Si resonant pressure microsensor was carefully designed and simulated numerically, where the use of the silicon cap was shown to effectively address temperature disturbances of the microsensor. The microsensor was then fabricated based on MEMS processes where eutectic bonding was adopted to link the SOI wafer and the silicon cap. The characterization results showed that the temperature disturbances of the resonant pressure microsensor encapsulated with the silicon cap were quantified as -0.82 Hz/°C of the central resonator and -2.36 Hz/°C of the side resonator within a temperature range from -40 °C to 80 °C, which were at least eight times lower than that of the microsensor encapsulated with the glass cap. Compared with the microsensor using the glass cap, the all-silicon microsensor demonstrated an accuracy improvement from 0.03% FS to 0.01% FS and a reduction in short-term frequency fluctuations from 3.2 Hz to 1.5 Hz.