Developing an abnormal high-Na-content P2-type layered oxide cathode with near-zero-strain for high-performance sodium-ion batteries.

Layered transition metal oxides (NaxTMO2) possess attractive features such as large specific capacity, high ionic conductivity, and a scalable synthesis process, making them a promising cathode candidate for sodium-ion batteries (SIBs). However, NaxTMO2 suffer from multiple phase transitions and Na+/vacancy ordering upon Na+ insertion/extraction, which is detrimental to their electrochemical performance. Herein, we developed a novel cathode material that exhibits an abnormal P2-type structure at a stoichiometric content of Na up to 1. The cathode material delivers a reversible capacity of 108 mA h g-1 at 0.2C and 97 mA h g-1 at 2C, retaining a capacity retention of 76.15% after 200 cycles within 2.0-4.3 V. In situ diffraction studies demonstrated that this material exhibits an absolute solid-solution reaction with a low volume change of 0.8% during cycling. This near-zero-strain characteristic enables a highly stabilized crystal structure for Na+ storage, contributing to a significant improvement in battery performance. Overall, this work presents a simple yet effective approach to realizing high Na content in P2-type layered oxides, offering new opportunities for high-performance SIB cathode materials.

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.

Objective: The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments. Patients and methods: This study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3-18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants' data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results. Results: Feature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity. Conclusions: This study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

Nickel Nanoparticles Protruding from Molybdenum Carbide Micropillars with Carbon Layer-Protected Biphasic 0D/1D Heterostructures for Efficient Water Splitting.

It remains a tremendous challenge to achieve high-efficiency bifunctional electrocatalysts for both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) for hydrogen production by water splitting. Herein, a novel hybrid of 0D nickel nanoparticles dispersed on the one-dimensional (1D) molybdenum carbide micropillars embedded in the carbon layers (Ni/Mo2C@C) was successfully prepared on nickel foam by a facile pyrolysis strategy. During the synthesis process, the nickel nanoparticles and molybdenum carbide were simultaneously generated under H2 and C2H2 mixed atmospheres and conformally encapsulated in the carbon layers. Benefiting from the distinctive 0D/1D heterostructure and the synergistic effect of the biphasic Mo2C and Ni together with the protective effect of the carbon layer, the reduced activation energy barriers and fast catalytic reaction kinetics can be achieved, resulting in a small overpotential of 96 mV for the HER and 266 mV for the OER at the current density of 10 mA cm-2 together with excellent durability in 1.0 M KOH electrolyte. In addition, using the developed Ni/Mo2C@C as both the cathode and anode, the constructed electrolyzer exhibits a small voltage of 1.55 V for the overall water splitting. The novel designed Ni/Mo2C@C may give inspiration for the development of efficient bifunctional catalysts with low-cost transition metal elements for water splitting.

Facilitating Layered Oxide Cathodes Based on Orbital Hybridization for Sodium-Ion Batteries: Marvelous Air Stability, Controllable High Voltage, and Anion Redox Chemistry.

Layered oxides have become the research focus of cathode materials for sodium-ion batteries (SIBs) due to the low cost, simple synthesis process, and high specific capacity. However, the poor air stability, unstable phase structure under high voltage, and slow anionic redox kinetics hinder their commercial application. In recent years, the concept of manipulating orbital hybridization has been proposed to simultaneously regulate the microelectronic structure and modify the surface chemistry environment intrinsically. In this review, the hybridization modes between atoms in 3d/4d transition metal (TM) orbitals and O 2p orbitals near the region of the Fermi energy level (EF ) are summarized based on orbital hybridization theory and first-principles calculations as well as various sophisticated characterizations. Furthermore, the underlying mechanisms are explored from macro-scale to micro-scale, including enhancing air stability, modulating high working voltage, and stabilizing anionic redox chemistry. Meanwhile, the origin, formation conditions, and different types of orbital hybridization, as well as its application in layered oxide cathodes are presented, which provide insights into the design and preparation of cathode materials. Ultimately, the main challenges in the development of orbital hybridization and its potential for the production application are also discussed, pointing out the route for high-performance practical sodium layered oxide cathodes.

Sodium Layered/Tunnel Intergrowth Oxide Cathodes: Formation Process, Interlocking Chemistry, and Electrochemical Performance.

Manganese-based layered oxides are prospective cathode materials for sodium-ion batteries (SIBs) due to their low cost and high theoretical capacities. The biphasic intergrowth structure of layered cathode materials is essential for improving the sodium storage performance, which is attributed to the synergistic effect between the two phases. However, the in-depth formation mechanism of biphasic intergrowth materials remains unclear. Herein, the layered/tunnel intergrowth Na0.6MnO2 (LT-NaMO) as a model material was successfully prepared, and their formation processes and electrochemical performance were systematically investigated. In situ high-temperature X-ray diffraction displays the detailed evolution process and excellent thermal stability of the layered/tunnel intergrowth structure. Furthermore, severe structural strain and large lattice volume changes are significantly mitigated by the interlocking effect between the phase interfaces, which further enhances the structural stability of the cathode materials during the charging/discharging process. Consequently, the LT-NaMO cathode displays fast Na+ transport kinetics with a remarkable capacity retention of ∼70.5% over 300 cycles at 5C, and its assembled full cell with hard carbon also exhibits high energy density. These findings highlight the superior electrochemical performance of intergrowth materials due to interlocking effects between layered and tunnel structures and also provide unique insights into the construction of intergrowth cathode materials for SIBs.

A Universal Strategy Based on Bridging Microstructure Engineering and Local Electronic Structure Manipulation for High-Performance Sodium Layered Oxide Cathodes.

Due to their high capacity and sufficient Na+ storage, O3-NaNi0.5Mn0.5O2 has attracted much attention as a viable cathode material for sodium-ion batteries (SIBs). However, the challenges of complicated irreversible multiphase transitions, poor structural stability, low operating voltage, and an unstable oxygen redox reaction still limit its practical application. Herein, using O3-NaNi0.5Mn0.5-xSnxO2 cathode materials as the research model, a universal strategy based on bridging microstructure engineering and local electronic structure manipulation is proposed. The strategy can modulate the physical and chemical properties of electrode materials, so as to restrain the unfavorable and irreversible multiphase transformation, improve structural stability, manipulate redox potential, and stabilize the anion redox reaction. The effect of Sn substitution on the intrinsic local electronic structure of the material is articulated by density functional theory calculations. Meanwhile, the universal strategy is also validated by Ti substitution, which could be further extrapolated to other systems and guide the design of cathode materials in the field of SIBs.

Multi-omics analysis reveals underlying host responses in pediatric respiratory syncytial virus pneumonia.

Respiratory syncytial virus (RSV) is an important pathogen causing pneumonia in children. Few studies have used multi-omics data to investigate the pathogenies of RSV pneumonia. Here, metabolomics was first used to identify potential biomarkers for RSV diagnosis. In the training cohort, serum from 36 healthy controls (HCs), 45 RSV pneumonia children, and 32 infectious disease controls (IDCs) were recruited. After analyses, six metabolites had potential diagnostic value. Using an independent cohort of 49 subjects, two biomarkers (neuromedin N and histidyl-proline diketopiperazine) were validated. Next, multi-omics analysis were applied to analyze the pathogenies of RSV pneumonia. Accumulation of collagen in the serum of RSVs indicated that RSV infection could lead to increased levels of soluble collage. Activation of the complement system and imbalance in lipid metabolism were also observed in RSV patients. The multi-omics analysis presented here revealed the signature protein and metabolite changes in serum caused by RSV infection.

Serum-integrated omics reveal the host response landscape for severe pediatric community-acquired pneumonia.

OBJECTIVE: Community-acquired pneumonia (CAP) is the primary cause of death for children under five years of age globally. Hence, it is essential to investigate new early biomarkers and potential mechanisms involved in disease severity. METHODS: Proteomics combined with metabolomics was performed to identify biomarkers suitable for early diagnosis of severe CAP. In the training cohort, proteomics and metabolomics were performed on serum samples obtained from 20 severe CAPs (S-CAPs), 15 non-severe CAPs (NS-CAPs) and 15 healthy controls (CONs). In the verification cohort, selected biomarkers and their combinations were validated using ELISA and metabolomics in an independent cohort of 129 subjects. Finally, a combined proteomics and metabolomics analysis was performed to understand the major pathological features and reasons for severity of CAP. RESULTS: The proteomic and metabolic signature was markedly different between S-CAPs, NS-CAPs and CONs. A new serum biomarker panel including 2 proteins [C-reactive protein (CRP), lipopolysaccharide (LBP)] and 3 metabolites [Fasciculol C, PE (14:0/16:1(19Z)), PS (20:0/22:6(4Z, 7Z, 10Z, 13Z, 16Z, 19Z))] was developed to identify CAP and to distinguish severe pneumonia. Pathway analysis of changes revealed activation of the cell death pathway, a dysregulated complement system, coagulation cascade and platelet function, and the inflammatory responses as contributors to tissue damage in children with CAP. Additionally, activation of glycolysis and higher levels of nucleotides led to imbalanced deoxyribonucleotide pools contributing to the development of severe CAP. Finally, dysregulated lipid metabolism was also identified as a potential pathological mechanism for severe progression of CAP. CONCLUSION: The integrated analysis of the proteome and metabolome might open up new ways in diagnosing and uncovering the complexity of severity of CAP.

Diagnosis of obstructive sleep apnea in children based on the XGBoost algorithm using nocturnal heart rate and blood oxygen feature.

PURPOSE: Obstructive sleep apnea (OSA) is a serious type of obstructive sleep-disordered breathing (SDB) that can cause a series of adverse effects on children's cardiovascular, growth, cognition, etc. The gold standard for diagnosis is polysomnography (PGS), which is used to assess the prevalence of OSA by obtaining the apnea-hypopnea index (AHI), but this diagnosis method is expensive and needs to be performed in a specialized laboratory, making it difficult to be of benefit to children with suspected OSA on a large scale. Our goal was to use a machine learning method to identify children with OSA of varying severity using data on children's nighttime heart rate and blood oxygen data. METHODS: This study included 3139 children who received diagnostic PSG with suspected OSA. Age, sex, BMI, 3 % oxygen depletion index (ODI), average nighttime heart rate and fastest heart rate were used as predictive features. Data sets were established with AHI ≥ 1, AHI ≥ 5, and AHI ≥ 10 as the diagnostic criteria for mild, moderate and severe OSA, and the samples of each data set were randomly divided into a training set and a test set at a ratio of 8:2. An OSA diagnostic model was established based on the XGBoost algorithm, and the ability of the machine learning model to diagnose OSA children with different severities was evaluated through different classification ability evaluation indicators. As a comparison, traditional classifier Logistic Regression was used to perform the same diagnostic task. The SHAP algorithm was used to evaluate the role of these features in the classification task. RESULTS: We established a diagnostic model of OSA in children based on the XGBoost algorithm. On the test set, the AUCs of the model for diagnosing mild, moderate, and severe OSA were 0.95, 0.88, and 0.88, respectively, and the classification accuracy was 90.45 %, 85.67 %, and 89.81 %, respectively, perform better than Logistic Regression classifiers. ODI is the most important feature in all classification tasks, and a higher fastest heart rate and ODI make the model tend to classify samples as positive. A high BMI value caused the model to tend to classify samples as positive in the mild and moderate classification tasks and as negative in the classification task with severe OSA. CONCLUSION: Using heart rate and blood oxygen data as the main features, a machine learning diagnostic model based on the XGBoost algorithm can accurately identify children with OSA at different severities. This diagnostic modality reduces the number of signals and the complexity of the diagnostic process compared to PSG, which could benefit children with suspected OSA who do not have the opportunity to receive a diagnostic PSG and provide a diagnostic priority reference for children awaiting a diagnostic PSG.

Influenza vaccination features revealed by a single-cell transcriptome atlas.

Emerging and re-emerging viruses like influenza virus pose a continuous global public health threat. Vaccines are one of the most effective public health strategies for controlling infectious diseases. However, little is known about the immunological features of vaccination at the single-cell resolution, including for influenza vaccination. Here, we report the single-cell transcriptome atlas of longitudinally collected peripheral blood mononuclear cells (PBMCs) in individuals immunized with an inactivated influenza vaccine. Overall, vaccination with the influenza vaccine only had a small impact on the composition of peripheral immune cells, but elicited global transcriptional changes in multiple immune cell subsets. In plasma and B cell subsets, transcriptomic changes, which were mostly involved in antibody production as well as B cell activation and differentiation, were observed after influenza vaccinations. In influenza-vaccinated individuals, we found a reduction in multiple biological processes (e.g., interferon response, inflammatory response, HLA-I/II molecules, cellular apoptosis, migration, and cytotoxicity, etc.,) 7 days postvaccination in multiple immune cell subsets. However, 14 days postvaccination, these levels returned to similar levels observed in prevaccination samples. Additionally, we did not observe significant upregulation of pro-inflammatory response genes and key thrombosis-related genes in influenza-vaccinated individuals. Taken together, we report a cell atlas of the peripheral immune response to influenza vaccination and provide a resource for understanding the immunological response mechanisms of influenza vaccination.

Multiple cross displacement amplification combined with nanoparticle-based lateral flow biosensor for rapid and sensitive detection of Epstein-Barr virus.

Introduction: Epstein-Barr virus (EBV) is a highly dangerous virus that is globally prevalent and closely linked to the development of nasopharyngeal cancer (NPC). Plasma EBV DNA analysis is an effective strategy for early detection, prognostication and monitoring of treatment response of NPC. Methods: Here, we present a novel molecular diagnostic technique termed EBV-MCDA-LFB, which integrates multiple cross displacement amplification (MCDA) with nanoparticle-based lateral flow (LFB) to enable simple, rapid and specific detection of EBV. In the EBV-MCDA-LFB system, a set of 10 primers was designed for rapidly amplifying the highly conserved tandem repeat BamHI-W region of the EBV genome. Subsequently, the LFB facilitate direct assay reading, eliminating the use of extra instruments and reagents. Results: The outcomes showed that the 65°C within 40 minutes was the optimal reaction setting for the EBV-MCDA system. The sensitivity of EBV-MCDA-LFB assay reached 7 copies per reaction when using EBV recombinant plasmid, and it showed 100% specificity without any cross-reactivity with other pathogens. The feasibility of the EBV-MCDA-LFB method for EBV detection was successfully validated by 49 clinical plasma samples. The complete detection process, consisting of rapid template extraction (15 minutes), MCDA reaction (65°C for 40 minutes), and LFB result reading (2 minutes), can be finalized within a 60-minutes duration. Discussion: EBV-MCDA-LFB assay designed here is a fast, extremely sensitive and specific technique for detecting EBV in field and at the point-of-care (PoC), which is especially beneficial for countries and regions with a high prevalence of the disease and limited economic resources.

Quantification of clofarabine in urine and plasma by LC-MS/MS: suitable for PK study and TDM in pediatric patients with relapsed or refractory ALL.

Clofarabine is approved for the treatment of relapsed or refractory acute lymphoblastic leukemia (ALL) in pediatric patients aged 1 to 21 years. Its pharmacokinetic (PK) exposure is strongly related to clinical outcomes and high risk of adverse reactions. PK-guided dosing of nucleoside analogs has the potential to improve survival and reduce toxicity in children. Considering that blood collection is an invasive operation and that the volume of blood collected is usually limited in pediatric ALL patients, a convenient and efficient method for the quantification of clofarabine in human urine and plasma was established with an LC-MS/MS system. Standard curves were shown to be liner in the range of 2.00-1000.00 ng mL-1 in both urine and plasma. Analytical validation of the assay included the assessment of linearity, accuracy (RE: -6.62% to 2.32%), intra-assay precision (RSD: 0.81% to 3.87%) and inter-assay precision (RSD: 1.88% to 5.69%). The absolute recovery rates of clofarabine were 85.50 ± 4.80%, 89.40 ± 0.70% and 98.00 ± 0.40% in urine and were 80.76 ± 1.88%, 86.81 ± 0.75%, 88.10 ± 0.61% in plasma at 5.00, 30.00 and 800.00 ng mL-1, respectively. The selectivity, stability and matrix effects conformed to the biological sample analysis requirements. The cumulative urine excretion rates for 24 hours of the three children with relapsed and refractory acute lymphoblastic leukemia were 72.22%, 87.88%, 82.16%, respectively. The PK data of the pediatric patient numbered lflb13-05 are very inconsistent with that of the other two children subjects, demonstrating that there may be an individual variation in Chinese pediatric patients, so the dose should be individualized based on the monitoring of drug concentration. The method is convenient, sensitive, and accurate, and it is suitable for the determination of clofarabine urine and plasma concentration. This is the first report on the pharmacokinetics of clofarabine in Chinese ALL children. Furthermore, it could be an alternative method to clinical monitoring of clofarabine.

SymMap database and TMNP algorithm reveal Huanggui Tongqiao granules for Allergic rhinitis through IFN-mediated neuroimmuno-modulation.

Allergic rhinitis (AR) is a series of reactions to allergen mediated by immunoglobulin E (IgE) and is one of the most common allergic diseases that affects children. Traditional Chinese Medicine, due to its diverse regulatory functions, may offer new strategies for AR therapy. Huanggui Tongqiao Granules (HTG) is a Chinese formula consisting of twelve herbs and has long been prescribed for patients with AR. The aim of this study is to determine the possible targets and action mechanisms of HTG for the AR treatment. SymMap database and TMNP algorithm were employed to show that interferon-gamma (IFN-gamma), acting as a molecular link between immunity and neural circuits, is the involved key target. The enrichment of immune and virus-related signaling pathways indicated the neuroimmunomodulatory potential of HTG. Then, AR mouse model was established by ovalbumin (OVA) challenge and was used to verify the therapeutic effects of HTG in vivo. HTG significantly relieved AR symptoms and nasal mucosal inflammation, reduced OVA-specific IgE levels and balanced IFN-gamma/IL-4 ratio. Moreover, transcriptional profile based on clinical data presented that blood cell-specific IFN-gamma co-expressed gene module (BIM) was underexpressed in AR patients, further validating the potential of IFN-gamma as target for AR. Collectively, these findings suggest that HTG could be a promising candidate drug for AR.

Night-to-night variability in respiratory sleep parameters to diagnose obstructive sleep apnea in children: A systematic review and meta-analysis.

PURPOSE: This systematic review aims to assess the night-to-night variability (NtNV) in respiratory sleep parameters in children and the accuracy of diagnosing obstructive sleep apnea (OSA) in children based on a single-night sleep study. METHODS: The PubMed, EMBASE, and Cochrane Library databases were searched until March 8, 2021. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42021239838). RESULTS: Our study included 395 patients from 5 articles. The mean (SD) age of all included patients was 11.78 (4.05) years. AHI was reported for 325 participants in 4 studies, and the mean change between two consecutive nights was -0.13 [95% CI: -0.40, 0.14] events per hour. The mean change in OAI was -0.07 [95% CI: -0.27, 0.12] events per hour in 187 participants across 3 studies. Based on the diagnostic criteria used, three studies reported that the diagnostic rates of OSA patients in a single-night sleep study were 83%, 84.6%, and 91%. The NtNV in AHI in children with severe and moderate OSA was greater than that in children with mild OSA (3.35 [95% CI: 0.07, 6.62] events per hour vs -0.15 [95% CI: -0.42, 0.12] events per hour), and these children with more severe OSA may have shown a higher AHI on the first night. CONCLUSIONS: The NtNV in AHI was not statistically significant in the group sample of children. However, there were significant differences in NtNV in AHI between children with mild and moderate-to-severe OSA. Individual NtNV in respiratory sleep parameters may cause children to be misdiagnosed by single-night diagnostic sleep studies.

Effects of obstructive sleep apnoea severity on neurocognitive and brain white matter alterations in children according to sex: a tract-based spatial statistics study.

OBJECTIVES: To investigate alterations in neurocognitive, attention, paediatric sleep questionnaire (PSQ) scores and whole brain white matter (WM) integrity between children with mild and severe obstructive sleep apnoea (OSA) according to sex and whether these changes are associated with OSA severity. METHODS: Fifty-seven children (36 males and 21 females) diagnosed with OSA were recruited for this study. Children of both sexes were divided into mild (male-MG, female-MG) and severe (male-SG, female-SG) groups according to OSA severity. Polysomnography (PSG), neurocognitive, attention and PSQ tests were compared between groups by one-way samples analysis of variance (ANOVA) F test. Diffusion tensor imaging (DTI) was scanned using a 3T GE MRI scanner and analysed by Tract-based Spatial Statistics (TBSS). Spearman correlation was calculated between DTI Eigenvalues and clinical characteristics. RESULTS: Compared to mild OSA patients, severe OSA patients presented greater severity of obstructive apnoea hypopnea index (OAHI), neurocognition, PSQ and attention tests in both male and female patients. Brain WM integrity in the male-SG, compared to the male-MG, demonstrated significantly reduced fractional anisotropy (FA) values in the right middle frontal gyrus and the right frontal sub-gyral regions and increased axial diffusivity (AD) values in the right inferior frontal gyrus, left parietal angular gyrus and sub-gyral regions, while no differences were found between the female-MG and female-SG. Alterations in male-SG brain regions were observably correlated with severity in male OSA patients. CONCLUSIONS: The integrity of WM, which regulates autonomic, cognitive, and attention functions, is impaired in male, but not female, children with severe OSA.

Disease characteristics and neuropathological changes associated with cognitive dysfunction in obstructive sleep apnea.

Obstructive sleep apnea (OSA) is a common sleep-disordered breathing disease that often leads to many comorbidities (e.g., cognitive dysfunction), which adversely affect the quality of life for patients with OSA. Thus far, the underlying mechanisms of this dysfunction remain unclear. Many studies have focused on OSA-related characteristics, including intermittent hypoxemia and sleep fragmentation. There is increasing emphasis on neuroimaging studies to explore underlying relationships between neuropathological changes and cognitive dysfunction. This article reviews recent research progress concerning cognitive dysfunction associated with OSA to reveal potential mechanisms that contribute to this dysfunction.