Chitosan and sodium alginate nanocarrier system: Controlling the release of rapeseed-derived peptides and improving their therapeutic efficiency of anti-diabetes.

Rapeseed-derived peptides (RPPs) can maintain the homeostasis of human blood glucose by inhibiting Dipeptidyl Peptidase-IV (DPP-IV) and activating the calcium-sensing receptor (CaSR). However, these peptides are susceptible to hydrolysis in the gastrointestinal tract. To enhance the therapeutic potential of these peptides, we developed a chitosan/sodium alginate-based nanocarrier to encapsulate two RPP variants, rapeseed-derived cruciferin peptide (RCPP) and rapeseed-derived napin peptide (RNPP). A convenient three-channel device was employed to prepare chitosan (CS)/sodium alginate (ALG)-RPPs nanoparticles (CS/ALG-RPPs) at a ratio of 1:3:1 for CS, ALG, and RPPs. CS/ALG-RPPs possessed optimal encapsulation efficiencies of 90.7 % (CS/ALG-RNPP) and 91.4 % (CS/ALG-RCPP), with loading capacities of 15.38 % (CS/ALG-RNPP) and 16.63 % (CS/ALG-RCPP) at the specified ratios. The electrostatic association between CS and ALG was corroborated by zeta potential and near infrared analysis. 13C NMR analysis verified successful RPPs loading, with CS/ALG-RNPP displaying superior stability. Pharmacokinetics showed that both nanoparticles were sustained release and transported irregularly (0.43 < n < 0.85). Compared with the control group, CS/ALG-RPPs exhibited significantly increased glucose tolerance, serum GLP-1 (Glucagon-like peptide 1) content, and CaSR expression which play pivotal roles in glucose homeostasis (*p < 0.05). These findings proposed that CS/ALG-RPPs hold promise in achieving sustained release within the intestinal epithelium, thereby augmenting the therapeutic efficacy of targeted peptides.

Metal-Centered Boron-Wheel Cluster of Y©B112- with Rare D11h Symmetry.

Molecules with high point-group symmetry are interesting prototype species in the textbook. As transition metal-centered boron clusters tend to have highly symmetric structures to fulfill multicenter bonding and high stability, new boron clusters with rare point-group symmetry may be viable. Through in-depth scrutiny over the structures of experimentally already observed transition metal-centered boron-wheel complexes, geometric and electronic design principles are summarized, based on which we studied M©B11k- (M = Y, La; Zr, Hf; k = 1, 2) clusters and found that a Y©B112- boron-wheel complex has an unprecedented D11h point-group symmetry. The remarkable stability of the planar Y©B112- complex is illustrated via extensive global-minimum structural search as well as comprehensive chemical bonding analyses. Similar to other boron-wheel complexes, the Y©B112- complex is shown to possess σ and π double aromaticity, indeed following the electronic design principle previously summarized. This new compound is expected to be experimentally identified, which will extend the currently known largest possible planar molecular symmetry and enrich the metal-centered boron-wheel class.

A UV-Vis spectroscopic detection method for cobalt ions in zinc sulfate solution based on discrete wavelet transform and extreme gradient boosting.

Zinc is a crucial strategic metal resource. The concentration of cobalt ions in zinc refining solution significantly impacts the efficiency of zinc electrolysis production. The traditional method of detecting cobalt ions in zinc solution is time-consuming, labor-intensive and ineffective. However, optical detection offers the advantage of high efficiency and low cost, making it a potential replacement for the traditional method. In this study, the spectral curve of cobalt ions in zinc solution is detected by ultraviolet-visible (UV-Vis) spectrophotometry. Additionally, we propose a model for the concentration-absorbance relationship of cobalt ions in zinc solution based on discrete wavelet transform and extreme gradient boosting (DWT-XGBoost) algorithms. First, the spectral curve's information region is denoised by using Savitzky-Golay (S-G) smoothing. Then, the denoised spectra is utilized to extract features through discrete wavelet transform and principal component analysis. These features are used as inputs to the XGBoost model to establish prediction models for low and high cobalt ions in zinc solution. Bayesian optimization is implemented to adjust the model's hyperparameters, including learning rate, feature sampling ratio, to enhance the prediction performance. Finally, applying the model to zinc solution samples from a zinc smelter and compared with other state-of-the-art algorithms, the DWT-XGBoost algorithm exhibits the lowest RMSE, MAE and MAPE, with values of 0.034 mg/L, 0.025 mg/L, 6.983 % for low cobalt and with values of 0.231 mg/L, 0.067 mg/L and 0.472 % for high cobalt. The experimental results demonstrate that the DWT-XGBoost model exhibits significantly superior prediction performance.

Single-cell multi-omics analysis of lineage development and spatial organization in the human fetal cerebellum.

Human cerebellum encompasses numerous neurons, exhibiting a distinct developmental paradigm from cerebrum. Here we conducted scRNA-seq, scATAC-seq and spatial transcriptomic analyses of fetal samples from gestational week (GW) 13 to 18 to explore the emergence of cellular diversity and developmental programs in the developing human cerebellum. We identified transitory granule cell progenitors that are conserved across species. Special patterns in both granule cells and Purkinje cells were dissected multidimensionally. Species-specific gene expression patterns of cerebellar lobes were characterized and we found that PARM1 exhibited inconsistent distribution in human and mouse granule cells. A novel cluster of potential neuroepithelium at the rhombic lip was identified. We also resolved various subtypes of Purkinje cells and unipolar brush cells and revealed gene regulatory networks controlling their diversification. Therefore, our study offers a valuable multi-omics landscape of human fetal cerebellum and advances our understanding of development and spatial organization of human cerebellum.

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.

Dual-Ligand Strategy to Construct Metal Organic Gel Catalyst with the Optimized Electronic Structure for High-Efficiency Overall Water Splitting and Flexible Metal-Air Battery.

Non-noble metal catalysts are known for their efficient catalytic performance for oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER). Metal organic gels (MOGs) can be considered as a promising electrocatalyst owing to the diverse physicochemical properties but usually suffer from its poor electrical conductivity and catalytic stability. Here, a FeCo-MOG is constructed with considerable trifunctional activity. The optimal P-CoFe-H3 prepared by using phytic acid (PA) and 2,4,6-Tris[(p-carboxyphenyl)amino]-1,3,5-triazine benzoic acid (H3TATAB) as dual ligands), exhibits outstanding ORR, OER, and HER activities as well as stability, exceeding most of state-of-the-art catalysts. As expected, the flexible Zn-air battery applied with P-CoFe-H3 as air cathode displays considerable power density, discharge voltage plateau, and cycling stability. Impressively, it is also capable of driving the overall water-splitting device by applying the P-CoFe-H3 as anode and cathode. Furthermore, theoretical calculations reveal that dual ligands can optimize the coordination environment and charge density of active sites, thereby reducing the absorption energy of intermediate species and boosting the catalytic performance. This work endows the dual-ligands coordination strategy with great potentiality for MOGs-based electrocatalysts in energy conversion devices.

Pulmonary administration of tetrandrine loaded Zinc-Alginate nanogels attenuates pulmonary fibrosis in rats.

Pulmonary fibrosis is a chronic and progressive disease, current systemic administration is not fully effective with many side effects, such as gastrointestinal and liver injury. The pulmonary delivery system for pulmonary fibrosis may contribute to maximize therapeutic benefit. Natural compounds might have prominence as potential drug candidates, but the low bioavailabilities affect their clinical use. Tetrandrine is a natural alkaloid with good anti-inflammatory, antifibrogenetic and antioxidant effects, and it is used as a clinical therapeutic drug for the treatment of silicosis in China. In the present study, we explore a new strategy of pulmonary delivery system to improve low solubility and pesticide effect of tetrandrine. Tetrandrine was loaded into alginate nanogels by reverse microemulsion method. The release behavior of tetrandrine reached zero-order kinetics release and the maximum free radical clearance rates reached up to 90%. The pulmonary fibrosis rats were treated with tetrandrine nanogels by using ultrasonic atomizing inhalation. Tetrandrine nanogels decreased the development and progression of fibrosis by reducing inflammation response and bating the deposition of extra cellular matrix. In conclusion, ultrasonic atomizing inhalation of tetrandrine nanogels provided a new therapeutic strategy for pulmonary fibrosis.

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.

Laser-Textured Hybrid Brass Pattern Array Surface for High-Efficiency Fog Collection.

Fog collection holds promise for addressing water shortage. However, the conventional fabrication of fog collection devices, normally chemical methods, suffers many challenges, such as complicated preparation and environmental issues. Herein, we proposed a green fabrication strategy to construct superhydrophobic/hydrophilic surfaces on the brass substrate via the combination of laser fabrication and heat treatment. The wettability of brass is directly dictated by the laser process parameters. The different superhydrophobic/hydrophilic hybrid pattern surface with a rectangular/triangular array was designed for an optimal fog collection performance. The maximum water collection efficiency of the prepared surface is measured up to 427.36 mg h-1 cm-2, which is 97% higher than that of the control sample. Furthermore, the surface can be folded into different forms to realize a flexible collector. We envision that our work provides a green fabrication strategy to construct a superwetting surface for highly efficient fog collection.

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.

Identification and Mechanism Elucidation of Anti-Inflammatory Peptides in Jinhua Ham: An Integrative In Silico and In Vitro Study.

This study aimed to effectively identify anti-inflammatory peptides in Jinhua ham, a dry-cured meat product made from the hind legs of pigs by curing and fermenting processes, and elucidate their anti-inflammatory mechanism. The investigation involved a combination of chromatographic purification, in silico screening, and in vitro validation. The first peak of JHP (JHP-P1) was purified using two-part exchange chromatography, in which 3350 peptides were identified by nano-HPLC-MS/MS, among which QLEELKR and EAEERADIAESQVNKLR showed significant anti-inflammatory potential (prediction scores: 0.759 and 0.841). In molecular docking and in vitro RAW264.7 cell experiments, these peptides displayed a strong affinity for Toll-like receptor 4-myeloid differentiation-2 (TLR4-MD-2), specifically binding around Arg 380, Lys 475, His 401, Gln 423, Asp 426, etc. This binding inhibited TLR4 expression and prevented trimer formation about TLR4-MD-2 and lipopolysaccharide (LPS), strongly inhibiting the inflammatory cascade. JHP suppressed LPS-induced cytokine overproduction and partially inhibited the phosphorylation of proteins in the MAPK/NF-κB pathway. These results demonstrated that combining in silico methods (activity prediction and molecular docking) is an effective strategy for screening anti-inflammatory peptides. This study provided a theoretical basis for identifying more anti-inflammatory peptides and applying them in functional foods.

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.

Diagnostic Performance and Clinical Impact of Metagenomic Next-Generation Sequencing for Pediatric Infectious Diseases.

Metagenomic next-generation sequencing (mNGS) has shown promise in the diagnosis of infectious diseases in adults, while its efficacy in pediatric infections remains uncertain. We performed a retrospective analysis of 1,493 mNGS samples from pediatric patients with blood, central nervous system, and lower respiratory tract infections. The positive percent agreement (PPA) and the negative percent agreement (NPA) of mNGS were compared to conventional microbiological tests (CMT) based on clinical diagnosis. The agreement of mNGS compared to CMT, as well as the clinical impact of mNGS, were valuated. Using the clinical diagnosis as a reference, mNGS demonstrated a significantly higher overall PPA compared to CMT (53.1% [95% CI = 49.7 to 56.6%] versus 25.8% [95% CI = 22.8 to 28.9%]), while maintaining a comparable overall NPA (93.2% [95% CI = 91.3 to 95.1%] versus 97.2% [95% CI = 95.9 to 98.4%]). In septic patients under 6 years of age or with immunosuppressive status, mNGS showed a higher PPA and a comparable NPA compared to CMT. The overall PPA and NPA of mNGS compared to CMT were 75.3 and 75.0%, respectively. The majority of cases of Streptococcus pneumoniae, Streptococcus agalactiae, Mycobacterium tuberculosis complex, and Pneumocystis jirovecii infections were identified by mNGS. A positive clinical impact of 14.0% (206/1,473), a negative impact of 0.8% (11/1,473), a nonimpact of 84.7% (1,248/1,473), and an unknown impact of 0.5% (8/1,473) were observed in the mNGS results. Notably, the positive impact was greater among immunosuppressed patients than among nonimmunosuppressed individuals (67/247, 27.1% versus 139/1,226, 11.3%; P < 0.001). mNGS is valuable for pathogen detection, diagnosis, and clinical management of infections among pediatric patients. mNGS was thus effective for the diagnosis of pediatric infections, which may guide clinical management. Patients with immunosuppressive conditions benefited more from mNGS testing.

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).

NetBID2 provides comprehensive hidden driver analysis.

Many signaling and other genes known as "hidden" drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .

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.