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.

Kelp as a biomonitor of persistent organic pollutants in coastal areas of China: Contamination levels and human health risk.

Kelp, the brown alga distributed in coastal areas all over the world, is also an important medicine food homology product in China. However, the levels and profiles of persistent organic pollutants (POPs) in kelp have not been thoroughly investigated to date. Polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs) and emerging bromine flame retardants (eBFRs) were evaluated in 41 kelp samples from the main kelp producing areas in China. The concentrations of total PCBs, PBDEs and eBFRs were in the range of 0.321-4.24 ng/g dry weight (dw), 0.255-25.5 ng/g dw and 3.00 × 10-3-47.2 ng/g dw in kelp, respectively. The pollutant pattern was dominated by decabromodiphenyl ethane (DBDPE, 13.0 ± 11.7 ng/g dw) followed in decreasing order by BDE-209 (2.74 ± 4.09 ng/g dw), CB-11 (1.32 ± 1.06 ng/g dw). The tested results showed that kelp could reflect the pollution status of PCBs, PBDEs and eBFRs, indicating the suitability of kelp as a biomonitor of these harmful substances. Finally, the data obtained was used to evaluate human non-cancer and cancer risks of PCBs and PBDEs via kelp consumption for Chinese. Though the calculated risk indices were considered acceptable according to the international standards even in the worst scenarios, the POPs levels in kelp should be monitored continuously as a good environmental indicator.

Diffusion kurtosis imaging tractography reveals disrupted white matter structural networks in children with obstructive sleep apnea syndrome.

To assess the disruptions of brain white matter (WM) structural network in children with obstructive sleep apnea (OSA) using diffusion kurtosis imaging (DKI). We use DKI tractography to construct individual whole-brain, region-level WM networks in 40 OSA and 28 healthy children. Then, we apply graph theory approaches to analyze whether OSA children would show altered global and regional network topological properties and whether these alterations would significantly correlate with the clinical characteristics of OSA. We found that both OSA and healthy children showed an efficient small-world organization and highly similar hub distributions in WM networks. However, characterized by kurtosis fractional anisotropy (KFA) weighted networks, OSA children exhibited decreased global and local efficiency, increased shortest path length compared with healthy children. For regional topology, OSA children exhibited significant decreased nodal betweenness centrality (BC) in the bilateral medial orbital superior frontal gyrus (ORBsupmed), right orbital part superior frontal gyrus (ORBsup), insula, postcentral gyrus, left middle temporal gyrus (MTG), and increased nodal BC in the superior parietal gyrus, pallidum. Intriguingly, the altered nodal BC of multiple regions (right ORBsupmed, ORBsup and left MTG) within default mode network showed significant correlations with sleep parameters for OSA patients. Our results suggest that children with OSA showed decreased global integration and local specialization in WM networks, typically characterized by DKI tractography and KFA metric. This study may advance our current understanding of the pathophysiological mechanisms of impaired cognition underlying OSA.

Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning.

The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluate these hazards, especially for chronic risk. In this study, machine learning (ML) was employed to construct quantitative structure-activity relationship (QSAR) models, enabling the prediction of chronic toxicity to aquatic organisms by leveraging the molecular characteristics of pollutants, namely, the molecular descriptors, fingerprints, and graphs. The limited dataset size hindered the notable advantages of the graph attention network (GAT) model for the molecular graphs. Considering computational efficiency and performance (R2 = 0.78; RMSE = 0.77), XGBoost (XGB) was used for reliable QSAR-ML models predicting chronic toxicity using small- or medium-sized tabular data and the molecular descriptors. Further kernel density estimation analysis confirmed the high accuracy of the model for pollutant concentrations ranging from 10-3 to 102 mg/L, effectively aligning with most environmental scenarios. Model interpretation showed SlogP and exposure duration as the primary influential factors. SlogP, representing the distribution coefficient of a molecule between lipophilic and hydrophilic environments, had a negative effect on the toxicity outcomes. Additionally, the exposure duration played a crucial role in determining the chronic toxicity. Finally, the chronic toxicity data of bisphenol A validated the robustness and reliability of the model established in this research. Our study provided a robust and feasible methodology for chronic ecological risk evaluation of various types of pollutants and could facilitate and increase the use of ML applications in environmental fields.

Structural identification of the related substances in phloroglucinol injection by two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry / 药学学报

This study aimed to identify the related substances of phloroglucinol injection by two-dimensional liquid chromatography quadrupole time-of-flight mass spectrometry (2D-LC-Q-TOF/MS). The first-dimensional separation was carried out on an HSS T3 (250 mm × 4.6 mm, 5 μm) column by gradient elution using 1.36 g·L-1 potassium dihydrogen phosphate buffer solution (pH adjusted to 3.0 with diluted phosphoric acid) and acetonitrile as the mobile phases. The separated components were then trapped in switch valve tube lines respectively and delivered to the second-dimensional desalting gradient elution which was performed with a BDS C18 (100 mm × 4.6 mm, 2.4 μm) column using 0.1% formic acid and methanol as the mobile phases. After rapid desalting, electrospray-ionization quadrupole time-of-flight high resolution mass spectrometry was used for determining the accurate masses and elemental compositions of the parents and their product ions for both phloroglucinol and its related substance. Structures of the related substances were then figured out by mass spectrometry elucidation, organic reaction mechanism analysis, and/or comparison with reference substances. Under the established analytical conditions, phloroglucinol and its related substances were adequately separated, 17 main related substances were detected and identified in the injection and its stressed samples for the first time. The identification results can provide reference for the quality control of phloroglucinol injection.

Novel STAMBP mutations in a Chinese girl with rare symptoms of microcephaly-capillary malformation syndrome and Mowat-Wilson syndrome.

Microcephaly-capillary malformation syndrome (MIC-CAP) and Mowat-Wilson syndrome (MWS) are both rare hereditary diseases with several overlapping symptoms. We here report a Chinese patient simultaneously affected by MIC-CAP and MWS, presenting with moderate anaemia because of repeated, unilateral refractory epistaxis. The girl was initially diagnosed with MWS after discovery of a pathogenic nonsense mutation in ZEB2. Starting from the age of 3 years old, the child experienced repeated epistaxis on the right side without obvious incentive or trauma. The bleeding was quite difficult to stop and her hemoglobin dropped from 124 g/L to 64 g/L in three months. Both coagulation disorders and allergic rhinitis were excluded by extensive workup and experimental therapeutics. Retrospective genetic analysis revealed that she carried two novel compound heterozygous mutations in STAMBP (c.610T > C: p.Ser204Pro and c.945C > G: p.Asn315Lys). This case report demonstrates a rare presentation of MIC-CAP in the pediatric population and enriches the variant spectrum of STAMBP.

Causality Investigation between Gut Microbiota, Derived Metabolites, and Obstructive Sleep Apnea: A Bidirectional Mendelian Randomization Study.

Various studies have highlighted the important associations between obstructive sleep apnea (OSA) and gut microbiota and related metabolites. Nevertheless, the establishment of causal relationships between these associations remains to be determined. Multiple mendelian randomization (MR) analyses were performed to genetically predict the causative impact of 196 gut microbiota and 83 metabolites on OSA. Two-sample MR was used to assess the potential association, and causality was evaluated using inverse variance weighted (IVW), MR-Egger, and weighted median (WM) methods. Multivariable MR (MVMR) was employed to ascertain the causal independence between gut microbiota and the metabolites linked to OSA. Additionally, Cochran's Q test, the MR Egger intercept test and the MR Steiger test were used for the sensitivity analyses. The analysis of the 196 gut microbiota revealed that genus_Ruminococcaceae (UCG009) (PIVW = 0.010) and genus_Subdoligranulum (PIVW = 0.041) were associated with an increased risk of OSA onset. Conversely, Family_Ruminococcaceae (PIVW = 0.030), genus_Coprococcus2 (PWM = 0.025), genus_Eggerthella (PIVW = 0.011), and genus_Eubacterium (xylanophilum_group) (PIVW = 0.001) were negatively related to the risk of OSA. Among the 83 metabolites evaluated, 3-dehydrocarnitine, epiandrosterone sulfate, and leucine were determined to be potential independent risk factors associated with OSA. Moreover, the reverse MR analysis demonstrated a suggestive association between OSA exposure and six microbiota taxa. This study offers compelling evidence regarding the potential beneficial or detrimental causative impact of the gut microbiota and its associated metabolites on OSA risk, thereby providing new insights into the mechanisms of gut microbiome-mediated OSA development.

Single-cell transcriptomic analysis reveals a systemic immune dysregulation in COVID-19-associated pediatric encephalopathy.

Unraveling the molecular mechanisms for COVID-19-associated encephalopathy and its immunopathology is crucial for developing effective treatments. Here, we utilized single-cell transcriptomic analysis and integrated clinical observations and laboratory examination to dissect the host immune responses and reveal pathological mechanisms in COVID-19-associated pediatric encephalopathy. We found that lymphopenia was a prominent characteristic of immune perturbation in COVID-19 patients with encephalopathy, especially those with acute necrotizing encephalopathy (AE). This was characterized a marked reduction of various lymphocytes (e.g., CD8+ T and CD4+ T cells) and significant increases in other inflammatory cells (e.g., monocytes). Further analysis revealed activation of multiple cell apoptosis pathways (e.g., granzyme/perforin-, FAS- and TNF-induced apoptosis) may be responsible for lymphopenia. A systemic S100A12 upregulation, primarily from classical monocytes, may have contributed to cytokine storms in patients with AE. A dysregulated type I interferon (IFN) response was observed which may have further exacerbated the S100A12-driven inflammation in patients with AE. In COVID-19 patients with AE, myeloid cells (e.g., monocytic myeloid-derived suppressor cells) were the likely contributors to immune paralysis. Finally, the immune landscape in COVID-19 patients with encephalopathy, especially for AE, were also characterized by NK and T cells with widespread exhaustion, higher cytotoxic scores and inflammatory response as well as a dysregulated B cell-mediated humoral immune response. Taken together, this comprehensive data provides a detailed resource for elucidating immunopathogenesis and will aid development of effective COVID-19-associated pediatric encephalopathy treatments, especially for those with AE.

Gemcitabine enhances pharmacokinetic exposure of the major components of Danggui Buxue Decoction in rat via the promotion of intestinal permeability and down-regulation of CYP3A for combination treatment of non-small cell lung cancer.

CONTEXT: Danggui Buxue Decoction (DBD), a traditional Chinese medicine formula, has the potential to enhance the antitumor effect of gemcitabine in non-small cell lung cancer (NSCLC) treatment by increasing gemcitabine's active metabolites. However, whether gemcitabine affects the pharmacokinetics of DBD's major components remains unclear. OBJECTIVE: This study evaluates the herb-drug interaction between DBD's major components and gemcitabine and validates the underlying pharmacokinetic mechanism. MATERIALS AND METHODS: The pharmacokinetics of 3.6 g/kg DBD with and without a single-dose administration of 50 mg/kg gemcitabine was investigated in Sprague-Dawley rats. The effects of gemcitabine on intestinal permeability, hepatic microsomal enzymes in rat tissues, and CYP3A overexpressing HepG2 cells were determined using western blot analysis. RESULTS: The combination of gemcitabine significantly altered the pharmacokinetic profiles of DBD's major components in rats. The Cmax and AUC of calycosin-7-O-ß-d-glucoside notably increased through sodium-glucose transporter 1 (SGLT-1) expression promotion. The AUC of ligustilide and ferulic acid was also significantly elevated with the elimination half-life (t1/2) prolonged by 2.4-fold and 7.8-fold, respectively, by down-regulating hepatic CYP3A, tight junction proteins zonula occludens-1 (ZO-1) and occludin expression. DISCUSSION AND CONCLUSIONS: Gemcitabine could modulate the pharmacokinetics of DBD's major components by increasing intestinal permeability, enhancing transporter expression, and down-regulating CYP3A. These findings provide critical information for clinical research on DBD as an adjuvant for NSCLC with gemcitabine and help make potential dosage adjustments more scientifically and rationally.

Organic carbon source excites extracellular polymeric substances to boost Fe0-mediated autotrophic denitrification in mixotrophic system.

Fe0-mediated autotrophic denitrification (ADN) can be suppressed by iron oxide coverage resulting from Fe0 corrosion. The mixotrophic denitrification (MDN) coupling Fe0-mediated ADN with heterotrophic denitrification (HDN) can circumvent the weakening of Fe0-mediated ADN over operation time. But the interaction between HDN and Fe0-mediated ADN for nitrogen removal of secondary effluent with deficient bioavailable organics remains unclear. When the influent COD/NO3--N ratio increased from 0.0 to 1.8-2.1, the TN removal efficiency was promoted significantly. The increased carbon source did not inhibit ADN, but promoted ADN and HDN synchronously. The formation of extracellular polymeric substances (EPS) was also facilitated concomitantly. Protein (PN) and humic acid (HA) in EPS increased significantly, which capable of accelerating electron transfer of denitrification. Due to that the electron transfer of HDN occurs intracellularly, the EPS with the capacity of accelerating electron transfer had a negligible influence on HDN. But for Fe0-mediated ADN, the increased EPS as well as corresponding PN and HA facilitated TN and NO3--N removal significantly, while accelerated the electron release originating from Fe0 corrosion. The bioorganic-Fe complexes were generated on Fe0 surface after used, meaning that the soluble EPS and soluble microbial products (SMP) participated in the electron transfer of Fe0-mediated ADN. The coexistence of HDN and ADN denitrifiers demonstrated the synchronous enhancement of HDN and ADN by the external carbon source. From the perspective of EPS and related SMP, the insight of enhancing Fe0-mediated ADN by external carbon source is beneficial to implement high-efficiency MDN for organics-deficient secondary wastewater.

Megacity's pathway toward sustainable food waste management and its environmental performance in a developing country: Evidence from Shanghai, China.

Due to the far distance impact on resident, aquatic and soil ecosystem health, food waste disposal becomes the focus of municipal waste strategy in developing countries. Shanghai is a leading city in China, its evolution of food waste management can indicate the coming future of the nation. In this city, from 1986 to 2020, the open dumping, landfill and incineration of food waste had been gradually banned, replaced by centralized compost and anaerobic digestion and other recovery ways. This study selected ten scenarios, ever used for food/mixed waste disposal in Shanghai, and analyzed the environmental impact change during the period 1986-2020. Life cycle assessment showed that although the generation of food waste raised rapidly, the total environmental impact, which was dominated by Freshwater Aquatic Ecotoxicity Potential, displayed a sharp decrease as 96.09 %, and Global Warming Potential decreased by 28.14 %. Substantial efforts should be made to improve the collection rate of biogas and landfill gas to reduce environmental impact, and quality of residues from anaerobic digestion and compost plants should be improved and utilized in its legal ways. Driving forces for the goal of sustainable food waste management in Shanghai involved economic development, environmental regulations and the supporting national/local standards.

Quantifying the effect of socio-economic-geo drivers on the change of municipal waste disposal in China by an integrated TWFE-PRF-SDM methodology.

Municipal solid waste management and disposal in China have significantly evolved since 2000. Due to China's vast land area and significant socioeconomic and geographic disparities, nationwide waste management strategies need to be further evaluated. This study quantified the effect of social, economic, and geographic drivers on municipal waste disposal activities in 31 provincial-level administrative regions from 2003 to 2020, by establishing a methodology integrated by two-way fixed effects regression model, panel random forest, and spatial Durbin model. The results showed, in the past two decades, socio-economic-geo indicators significantly co-decided the landfill and incineration activities. In particular, the explanatory ability of GDP per capita was above 45%, while land resources and city size also showed great significance. Spatial autocorrelation analysis showed that the relationship between landfill/incineration rates and socio-economic-geo drivers changed notably from unobvious to significant with economic growth. Furthermore, the local economy and land resources displayed more impact than those of the neighboring regions, while sci-tech and education showed clear spatial spillover effects. Chinese government would carefully assess the full-scale promotion policy of incineration plants, landfill is still hold as a reasonable option for regions with specific socio-economic-geo conditions.

Systemic immune dysregulation in severe tuberculosis patients revealed by a single-cell transcriptome atlas.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) infection, is currently the deadliest infectious disease in human that can evolve to severe forms. A comprehensive immune landscape for Mtb infection is critical for achieving TB cure, especially for severe TB patients. We performed single-cell RNA transcriptome and T-cell/B-cell receptor (TCR/BCR) sequencing of 213,358 cells from 27 samples, including 6 healthy donors and 21 active TB patients with varying severity (6 mild, 6 moderate and 9 severe cases). Two published profiles of latent TB infection were integrated for the analysis. We observed an obviously elevated proportion of inflammatory immune cells (e.g., monocytes), as well as a markedly decreased abundance of various lymphocytes (e.g., NK and γδT cells) in severe patients, revealing that lymphopenia might be a prominent feature of severe disease. Further analyses indicated that significant activation of cell apoptosis pathways, including perforin/granzyme-, TNF-, FAS- and XAF1-induced apoptosis, as well as cell migration pathways might confer this reduction. The immune landscape in severe patients was characterized by widespread immune exhaustion in Th1, CD8+T and NK cells as well as high cytotoxic state in CD8+T and NK cells. We also discovered that myeloid cells in severe TB patients may involve in the immune paralysis. Systemic upregulation of S100A12 and TNFSF13B, mainly by monocytes in the peripheral blood, may contribute to the inflammatory cytokine storms in severe patients. Our data offered a rich resource for understanding of TB immunopathogenesis and designing effective therapeutic strategies for TB, especially for severe patients.

Application of the metal ions as potential population biomarkers for wastewater-based epidemiology: estimating tobacco consumption in Southern China.

Wastewater-based epidemiology (WBE) is an objective approach for the estimation of population-level exposure to a wide range of substances, in which the use of a population biomarker (PB) could significantly reduce back-calculation errors. Although some endogenous or exogenous compounds such as cotinine and other hormones have been developed as PBs, more PBs still need to be identified and evaluated. This study aimed to propose a novel method to estimate population parameters from the mass load of metal ion biomarkers in wastewater, and estimate the consumption of tobacco in 24 cities in Southern China using the developed method. Daily wastewater samples were collected from 234 wastewater treatment plants (WWTPs) in 24 cities in Southern China. Atomic absorption spectroscopy (AAS) was applied to determine the concentrations of common health-related metal ions in wastewater, including sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), iron (Fe), and zinc (Zn), and compared them with the daily mass load of cotinine corresponding to catchment populations. The concentrations of cotinine in wastewater samples were measured using liquid chromatography-tandem mass spectrometry. There were clear and strong correlations between the target metal ion equivalent population and census data. The correlation coefficients (R) were RK = 0.78, RNa = 0.66, RCa = 0.81, RMg = 0.77, and RFe = 0.69, at p < 0.01 and R2 > 0.6. Subsequently, the combination of WBE and metal ion PBs was used to estimate tobacco consumption. Daily consumption of nicotine was estimated to be approximately 1.76 ± 1.19 mg/d/capita, equivalent to an average of 13.0 ± 8.75 cigarettes/d being consumed by smokers. The data on tobacco consumption in this study were consistent with those in traditional surveys in Southern China. The metal ion potassium is an appropriate PB for reflecting the real-time population and could be used to evaluate the tobacco consumption in WBE study.

Evaluation of the household solid waste mandatory sorting policy in Shanghai: A 1-year public opinion survey.

Sustainable management of household solid waste (HSW) is an essential step in realizing the development of a circular economy (CE). However, for many developing countries, HSW sorting is evolving incrementally, and focusing on the intervention role of policy and the sorting behaviour of residents can help achieve sustainable waste management. This study aims to investigate the main determinants of residents' HSW sorting behaviour and how policy particularly contributes to it. Drawing upon the theory of planned behaviour (TPB) and incorporating experiences, a conceptual framework is tested on a sample of Chinese residents in Shanghai (n = 560, from November to December 2020) using structural equation modelling analysis. The empirical results show that experience and behavioural intention greatly influence HSW sorting behaviour. Moreover, the positive attitude of residents promotes the success of HSW sorting. Subjective norms and perceived behaviour control indirectly affect behavioural intention and behaviour via attitude. The findings of this study provide policymakers with critical factors for the successful implementation of HSW sorting. We also propose policy implications to maintain and improve sorting efficiency from the perspective of residents (education, communication and publicity), neighbourhood committees (degree and efficiency of supervision) and property management companies (good infrastructure, services and processes).

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.

S1PR1 serves as a viable drug target against pulmonary fibrosis by increasing the integrity of the endothelial barrier of the lung.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with unclear etiology and limited treatment options. The median survival time for IPF patients is approximately 2-3 years and there is no effective intervention to treat IPF other than lung transplantation. As important components of lung tissue, endothelial cells (ECs) are associated with pulmonary diseases. However, the role of endothelial dysfunction in pulmonary fibrosis (PF) is incompletely understood. Sphingosine-1-phosphate receptor 1 (S1PR1) is a G protein-coupled receptor highly expressed in lung ECs. Its expression is markedly reduced in patients with IPF. Herein, we generated an endothelial-conditional S1pr1 knockout mouse model which exhibited inflammation and fibrosis with or without bleomycin (BLM) challenge. Selective activation of S1PR1 with an S1PR1 agonist, IMMH002, exerted a potent therapeutic effect in mice with bleomycin-induced fibrosis by protecting the integrity of the endothelial barrier. These results suggest that S1PR1 might be a promising drug target for IPF therapy.

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.

Accuracy and capability of tri-ponderal mass index in assessing cardio-metabolic risk factors in Chinese children and adolescents aged 3 to 17 years, compared with body mass index.

BACKGROUND: Tri-ponderal mass index (TMI) has been reported to be a more accurate estimate of body fat than body mass index (BMI). This study aims to compare the effectiveness of TMI and BMI in identifying hypertension, dyslipidemia, impaired fasting glucose (IFG), abdominal obesity, and clustered cardio-metabolic risk factors (CMRFs) in 3- to 17-year-old children. METHODS: A total of 1587 children aged 3 to 17 years were included. Logistic regression was used to evaluate correlations between BMI and TMI. Area under the curves (AUCs) were used to compare discriminative capability among indicators. BMI was converted to BMI- z scores, and accuracy was compared by false-positive rate, false-negative rate, and total misclassification rate. RESULTS: Among children aged 3 to 17 years, the mean TMI was 13.57 ±â€Š2.50 kg/m 3 for boys and 13.3 ±â€Š2.33 kg/m 3 for girls. Odds ratios (ORs) of TMI for hypertension, dyslipidemia, abdominal obesity, and clustered CMRFs ranged from 1.13 to 3.15, higher than BMI, whose ORs ranged from 1.08 to 2.98. AUCs showed similar ability of TMI (AUC: 0.83) and BMI (AUC: 0.85) in identifying clustered CMRFs. For abdominal obesity and hypertension, the AUC of TMI was 0.92 and 0.64, respectively, which was significantly better than that of BMI, 0.85 and 0.61. AUCs of TMI for dyslipidemia and IFG were 0.58 and 0.49. When 85th and 95th of TMI were set as thresholds, total misclassification rates of TMI for clustered CMRFs ranged from 6.5% to 16.4%, which was not significantly different from that of BMI- z scores standardized according to World Health Organization criteria. CONCLUSIONS: TMI was found to have equal or even better effectiveness in comparison with BMI in identifying hypertension, abdominal obesity, and clustered CMRFs TMI was more stable than BMI in 3- to 17-year-old children, while it failed to identify dyslipidemia and IFG. It is worth considering the use of TMI for screening CMRFs in children and adolescents.